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1

AEO Assumptions  

Gasoline and Diesel Fuel Update (EIA)

for the for the Annual Energy Outlook 1997 December 1996 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 Energy Information Administration/Assumptions for the Annual Energy Outlook 1997 Contents Page Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Household Expenditures Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Commercial Demand Module . . . . . . . . . . . . . . . . . .

2

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2003, DOE/EIA-M060(2003) (Washington, DC, January 2003). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves addresses the relationship between the minemouth price of coal and corresponding levels of capacity utilization of mines, mining capacity, labor productivity, and the cost of factor inputs (mining equipment, mine labor, and fuel requirements).

3

Assumptions to the Annual Energy Outlook 1999 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

coal.gif (4423 bytes) coal.gif (4423 bytes) The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Model Documentation: Coal Market Module of the National Energy Modeling System, DOE/EIA-MO60. Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions, and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves addresses the relationship between the minemouth price of coal and corresponding levels of coal production, labor productivity, and the cost of factor inputs (mining equipment, mine labor, and fuel requirements).

4

Analysis of the Effects of Compositional and Configurational Assumptions on Product Costs for the Thermochemical Conversion of Lignocellulosic Biomass to Mixed Alcohols -- FY 2007 Progress Report  

DOE Green Energy (OSTI)

The purpose of this study was to examine alternative biomass-to-ethanol conversion process assumptions and configuration options to determine their relative effects on overall process economics. A process-flow-sheet computer model was used to determine the heat and material balance for each configuration that was studied. The heat and material balance was then fed to a costing spreadsheet to determine the impact on the ethanol selling price. By examining a number of operational and configuration alternatives and comparing the results to the base flow sheet, alternatives having the greatest impact the performance and cost of the overall system were identified and used to make decisions on research priorities.

Zhu, Yunhua; Gerber, Mark A.; Jones, Susanne B.; Stevens, Don J.

2008-12-05T23:59:59.000Z

5

Analysis of the Effects of Compositional and Configurational Assumptions on Product Costs for the Thermochemical Conversion of Lignocellulosic Biomass to Mixed Alcohols – FY 2007 Progress Report  

DOE Green Energy (OSTI)

The purpose of this study was to examine alternative biomass-to-ethanol conversion process assumptions and configuration options to determine their relative effects on overall process economics. A process-flow-sheet computer model was used to determine the heat and material balance for each configuration that was studied. The heat and material balance was then fed to a costing spreadsheet to determine the impact on the ethanol selling price. By examining a number of operational and configuration alternatives and comparing the results to the base flow sheet, alternatives having the greatest impact the performance and cost of the overall system were identified and used to make decisions on research priorities. This report, which was originally published in December 2008, has been revised primarily to correct information presented in Appendix B -- Base Case Flow Sheets and Model Results. The corrections to Appendix B include replacement of several pages in Table B.1 that duplicated previous pages of the table. Other changes were made in Appendix B to correct inconsistencies between stream labels presented in the tables and the stream labels in the figures.

Zhu, Yunhua; Gerber, Mark A.; Jones, Susanne B.; Stevens, Don J.

2009-02-01T23:59:59.000Z

6

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7, DOE/EIA-M060(2007) (Washington, 7, DOE/EIA-M060(2007) (Washington, DC, 2007). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, and the cost of factor inputs (labor and fuel).

7

Assumptions  

Gasoline and Diesel Fuel Update (EIA)

1 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Household Expenditures Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Commercial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Natural Gas Transmission and Distribution Module . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 Petroleum Market Module. . . . . . . . . . . . .

8

Assumptions  

Gasoline and Diesel Fuel Update (EIA)

to the to the Annual Energy Outlook 1998 December 1997 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Household Expenditures Module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Commercial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Oil and Gas Supply Module

9

Assumptions to the Annual Energy Outlook 2001 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2001, DOE/EIA-M060(2001) January 2001. Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions, and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves

10

Assumptions to the Annual Energy Outlook 2002 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2002, DOE/EIA-M060(2002) (Washington, DC, January 2002). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves

11

EIA - Assumptions to the Annual Energy Outlook 2009 - Coal Market...  

Annual Energy Outlook 2012 (EIA)

of mining equipment, the cost of factor inputs (labor and fuel), and other mine supply costs. The key assumptions underlying the coal production modeling are: As capacity...

12

Assumptions to the Annual Energy Outlook - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module Assumption to the Annual Energy Outlook Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2004, DOE/EIA-M060(2004) (Washington, DC, 2004). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves addresses the relationship between the minemouth price of coal and corresponding levels of capacity utilization of mines, mining capacity, labor productivity, and the cost of factor inputs (mining equipment, mine labor, and fuel requirements).

13

EIA - Assumptions to the Annual Energy Outlook 2009 - Petroleum Market  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumptions to the Annual Energy Outlook 2009 Petroleum Market Module Figure 9., Petroleum Administration for Defense Districts. Need help, contact the National Energy Information Center at 202-586-8800. Table 11.1. Petroleum Product Categories. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 11.2. Year Round Gasoline Specifications by Petroleum Administration for Defense Districts. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 11.3. Gasolline Sulfur Content Assumptions, by Region and Gasoline Type, Parts per Million (PPM). Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version

14

Annual Energy Outlook 96 Assumptions  

Gasoline and Diesel Fuel Update (EIA)

for for the Annual Energy Outlook 1996 January 1996 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 Introduction This paper presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 1996 (AEO96). In this context, assumptions include general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports listed in the Appendix. 1 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview. The National Energy Modeling System The projections

15

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2006, DOE/EIA-M060(2006) (Washington, DC, 2006). Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2006, DOE/EIA-M060(2006) (Washington, DC, 2006). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, and the cost of factor inputs (labor and fuel).

16

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for each Census Division using the assumptions and methods described below.106

17

Assumptions to the Annual Energy Outlook 2000 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2000, DOE/EIA-M060(2000) January 2000. The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2000, DOE/EIA-M060(2000) January 2000. Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions, and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves addresses the relationship between the minemouth price of coal and corresponding levels of coal production, labor productivity, and the cost of factor inputs (mining equipment, mine labor, and fuel requirements).

18

Crude Oil and Petroleum Products Total Stocks Stocks by Type  

U.S. Energy Information Administration (EIA)

Stock Type: Download Series History: Definitions, Sources & Notes: Show Data By: Product: Stock Type: Area: 2007 2008 2009 2010 2011 2012 View History; U.S. 1,665,345 ...

19

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Energy Module Energy Module This page inTenTionally lefT blank 21 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 International Energy Module The LFMM International Energy Module (IEM) simulates the interaction between U.S. and global petroleum markets. It uses assumptions of economic growth and expectations of future U.S. and world crude-like liquids production and consumption to estimate the effects of changes in U.S. liquid fuels markets on the international petroleum market. For each year of the forecast, the LFMM IEM computes BRENT and WTI prices, provides a supply curve of world crude-like liquids, and generates a worldwide oil supply- demand balance with regional detail. The IEM also provides, for each year of the projection period, endogenous and

20

Prognostic Evaluation of Assumptions Used by Cumulus Parameterizations  

Science Conference Proceedings (OSTI)

Using a spectral-type cumulus parameterization that includes moist downdrafts within a three-dimensional mesoscale model, various disparate closure assumptions are systematically tested within the generalized framework of dynamic control, static ...

Georg A. Grell

1993-03-01T23:59:59.000Z

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


21

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution network that links them. In addition, natural gas flow patterns are a function of the pattern in the previous year, coupled with the relative prices of gas supply options as translated to the represented market “hubs.” The major assumptions used within the NGTDM are grouped into five general categories. They relate to (1) the classification of demand into core and noncore transportation service classes, (2) the pricing of transmission and distribution services, (3) pipeline and storage capacity expansion and utilization, and (4) the implementation of recent regulatory reform. A complete listing of NGTDM assumptions and in-depth methodology descriptions are presented in Model Documentation: Natural Gas Transmission and Distribution Model of the National Energy Modeling System, Model Documentation 2003, DOE/EIA- M062(2003) (Washington, DC, January 2003).

22

The disciplined use of simplifying assumptions  

Science Conference Proceedings (OSTI)

Simplifying assumptions --- everyone uses them but no one's programming tool explicitly supports them. In programming, as in other kinds of engineering design, simplifying assumptions are an important method for dealing with complexity. Given a complex ...

Charles Rich; Richard C. Waters

1982-04-01T23:59:59.000Z

23

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Macroeconomic Activity Module Macroeconomic Activity Module This page inTenTionally lefT blank 17 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents interactions between the U.S. economy and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP), is a key determinant of growth in the demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected

24

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Demand Module Demand Module This page inTenTionally lefT blank 39 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2040. The definition of the commercial sector is consistent with EIA's State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial.

25

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Demand Module Demand Module This page inTenTionally lefT blank 27 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Residential Demand Module The NEMS Residential Demand Module projects future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the "unit energy consumption" (UEC) by appliance (in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing

26

Assumptions to the Annual Energy Outlook 2012  

U.S. Energy Information Administration (EIA)

Assumptions to the Annual Energy Outlook 2012 August 2012 www.eia.gov U.S. Department of Energy Washington, DC 20585

27

EIA - Assumptions to the Annual Energy Outlook 2008 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module Assumptions to the Annual Energy Outlook 2008 Coal Market Module The NEMS Coal Market Module (CMM) provides projections of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2008, DOE/EIA-M060(2008) (Washington, DC, 2008). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the projection. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, and the cost of factor inputs (labor and fuel).

28

EIA-Assumptions to the Annual Energy Outlook - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module Assumptions to the Annual Energy Outlook 2007 Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2007, DOE/EIA-M060(2007) (Washington, DC, 2007). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, and the cost of factor inputs (labor and fuel).

29

EIA - Assumptions to the Annual Energy Outlook 2010 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module Assumptions to the Annual Energy Outlook 2010 Coal Market Module The NEMS Coal Market Module (CMM) provides projections of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2010, DOE/EIA-M060(2010) (Washington, DC, 2010). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the projection. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, the cost of factor inputs (labor and fuel), and other mine supply costs.

30

Table 23. Coal Mining Productivity by State, Mine Type, and Mine Production Range, 2012  

U.S. Energy Information Administration (EIA) Indexed Site

Mining Productivity by State, Mine Type, and Mine Production Range, 2012 Mining Productivity by State, Mine Type, and Mine Production Range, 2012 (short tons produced per employee hour) U.S. Energy Information Administration | Annual Coal Report 2012 Table 23. Coal Mining Productivity by State, Mine Type, and Mine Production Range, 2012 (short tons produced per employee hour) U.S. Energy Information Administration | Annual Coal Report 2012 Mine Production Range (thousand short tons) Coal-Producing State, Region 1 and Mine Type Above 1,000 Above 500 to 1,000 Above 200 to 500 Above 100 to 200 Above 50 to 100 Above 10 to 50 10 or Under Total 2 Alabama 1.69 2.50 1.95 1.72 1.83 0.69 0.55 1.68 Underground 1.73 - - - 1.08 0.31 - 1.64 Surface 1.36 2.50 1.95 1.72 2.11 1.19 0.55 1.75 Alaska 5.98 - - - - - - 5.98 Surface 5.98 - - - - - - 5.98 Arizona 7.38 - - - - - - 7.38 Surface

31

EIA - Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

7 7 Assumptions to the Annual Energy Outlook 2007 This report summarizes the major assumptions used in the NEMS to generate the AEO2007 projections. Contents (Complete Report) Download complete Report. Need help, contact the National Energy Information Center at 202-586-8800. Introduction Introduction Section to the Assumptions to the Annual Energy Outlook 2007 Report. Need help, contact the National Energy Information Center at 202-586-8800. Introduction Section to the Assumptions to the Annual Energy Outlook 2007 Report. Need help, contact the National Energy Information Center at 202-586-8800. Macroeconomic Activity Module Macroeconomic Activity Module Section to the Assumptions to the Annual Energy Outlook 2007 Report. Need help, contact the National Energy Information Center at 202-586-8800.

32

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Household Expenditures Module Household Expenditures Module The Household Expenditures Module (HEM) constructs household energy expenditure profiles using historical survey data on household income, population and demographic characteristics, and consumption and expenditures for fuels for various end-uses. These data are combined with NEMS forecasts of household disposable income, fuel consumption, and fuel expenditures by end-use and household type. The HEM disaggregation algorithm uses these combined results to forecast household fuel consumption and expenditures by income quintile and Census Division. Key Assumptions The historical input data used to develop the HEM version for the AEO2003 consists of recent household survey responses, aggregated to the desired level of detail. Two surveys performed by the Energy Information Administration are included in the AEO2003 HEM database, and together these input data are used to develop a set of baseline household consumption profiles for the direct fuel expenditure analysis. These surveys are the 1997 Residential Energy Consumption Survey (RECS) and the 1991 Residential Transportation Energy Consumption Survey (RTECS).

33

Climate Action Planning Tool Formulas and Assumptions  

NLE Websites -- All DOE Office Websites (Extended Search)

CLIMATE ACTION PLANNING TOOL FORMULAS AND ASSUMPTIONS Climate Action Planning Tool Formulas and Assumptions The Climate Action Planning Tool calculations use the following formulas and assumptions to generate the business-as-usual scenario and the greenhouse gas emissions reduction goals for the technology options. Business-as-Usual Scenario All Scope 1 (gas, oil, coal, fleet, and electricity) and Scope 2 calculations increase at a rate equal to the building growth rate. Scope 3 calculations (commuters and business travel) increase at a rate equal to the population growth rate. Assumptions New buildings will consume energy at the same rate (energy use intensity) as existing campus buildings. Fleet operations will be proportional to total building area.

34

Hierarchy of Mesoscale Flow Assumptions and Equations  

Science Conference Proceedings (OSTI)

The present research proposes a standard nomenclature for mesoscale meteorological concepts and integrates existing concepts of atmospheric space scales, flow assumptions, governing equations, and resulting motions into a hierarchy useful in ...

P. Thunis; R. Bornstein

1996-02-01T23:59:59.000Z

35

Assumptions to the Annual Energy Outlook - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumption to the Annual Energy Outlook Petroleum Market Module Figure 8. Petroleum Administration for Defense Districts. Having problems, call our National Energy Information Center at 202-586-8800 for help. The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohols, ethers, and bioesters natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of U.S. refining

36

Crude Oil and Petroleum Products Total Stocks Stocks by Type  

U.S. Energy Information Administration (EIA) Indexed Site

Product: Crude Oil and Petroleum Products Crude Oil All Oils (Excluding Crude Oil) Pentanes Plus Liquefied Petroleum Gases Ethane/Ethylene Propane/Propylene Normal Butane/Butylene Isobutane/Butylene Other Hydrocarbons Oxygenates (excluding Fuel Ethanol) MTBE Other Oxygenates Renewables (including Fuel Ethanol) Fuel Ethanol Renewable Diesel Fuel Other Renewable Fuels Unfinished Oils Unfinished Oils, Naphthas & Lighter Unfinished Oils, Kerosene & Light Gas Unfinished Oils, Heavy Gas Oils Residuum Motor Gasoline Blending Comp. (MGBC) MGBC - Reformulated MGBC - Reformulated, RBOB MGBC - Reformulated, RBOB w/ Alcohol MGBC - Reformulated, RBOB w/ Ether MGBC - Reformulated, GTAB MGBC - Conventional MGBC - Conventional, CBOB MGBC - Conventional, GTAB MGBC - Conventional Other Aviation Gasoline Blending Comp. Finished Motor Gasoline Reformulated Gasoline Reformulated Gasoline Blended w/ Fuel Ethanol Reformulated Gasoline, Other Conventional Gasoline Conventional Gasoline Blended Fuel Ethanol Conventional Gasoline Blended Fuel Ethanol, Ed55 and Lower Conventional Other Gasoline Finished Aviation Gasoline Kerosene-Type Jet Fuel Kerosene Distillate Fuel Oil Distillate F.O., 15 ppm Sulfur and under Distillate F.O., Greater than 15 to 500 ppm Sulfur Distillate F.O., Greater 500 ppm Sulfur Residual Fuel Oil Residual F.O., than 1.00% Sulfur Petrochemical Feedstocks Naphtha for Petro. Feedstock Use Other Oils for Petro. Feedstock Use Special Naphthas Lubricants Waxes Petroleum Coke Asphalt and Road Oil Miscellaneous Products Period-Unit: Monthly-Thousand Barrels Annual-Thousand Barrels

37

Assumption Parish, Louisiana: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

Assumption Parish, Louisiana: Energy Resources Assumption Parish, Louisiana: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 29.9232544°, -91.09694° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":29.9232544,"lon":-91.09694,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

38

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Introduction Introduction This page inTenTionally lefT blank 3 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2013 [1] (AEO2013), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports [2]. The National Energy Modeling System Projections in the AEO2013 are generated using the NEMS, developed and maintained by the Office of Energy Analysis of the U.S.

39

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Assumptions to the Annual Assumptions to the Annual Energy Outlook 2013 May 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or other Federal agencies. Table of Contents Introduction .................................................................................................................................................. 3

40

Assumptions to the Annual Energy Outlook 1999 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

petroleum.gif (4999 bytes) petroleum.gif (4999 bytes) The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for each Census Division using the assumptions and methods described below. 75

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41

Assumptions to the Annual Energy Outlook 2008  

Gasoline and Diesel Fuel Update (EIA)

8) 8) Release date: June 2008 Next release date: March 2009 Assumptions to the Annual Energy Outlook 2008 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Commercial Demand Module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Natural Gas Transmission and Distribution Module. . . . . . . . . . . . . . . . . . . . . . 113 Petroleum Market Module

42

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to the Annual Energy Outlook 2004 Assumptions to the Annual Energy Outlook 2004 143 Appendix A: Handling of Federal and Selected State Legislation and Regulation in the Annual Energy Outlook Legislation Brief Description AEO Handling Basis Residential Sector A. National Appliance Energy Conservation Act of 1987 Requires Secretary of Energy to set minimum efficiency standards for 10 appliance categories a. Room Air Conditioners Current standard of 8.82 EER Federal Register Notice of Final Rulemaking, b. Other Air Conditioners (<5.4 tons) Current standard 10 SEER for central air conditioner and heat pumps, increasing to 12 SEER in 2006. Federal Register Notice of Final Rulemaking, c. Water Heaters Electric: Current standard .86 EF, incr easing to .90 EF in 2004. Gas: Curren

43

Assumptions to the Annual Energy Outlook - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumption to the Annual Energy Outlook Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20041 (AEO2004), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview3, which is updated once every two years. The National Energy Modeling System The projections in the AEO2004 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the midterm time period and perform policy analyses requested by decisionmakers in the U.S. Congress, the Administration, including DOE Program Offices, and other government agencies.

44

Assumptions to the Annual Energy Outlook 2000 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for each Census Division using the assumptions and methods described below.100

45

Energy Information Administration (EIA) - Assumptions to the...  

Gasoline and Diesel Fuel Update (EIA)

of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs...

46

Assumptions to the Annual Energy Outlook - Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumption to the Annual Energy Outlook Industrial Demand Module Table 17. Industry Categories Printer Friendly Version Energy-Intensive Manufacturing Nonenergy-Intensive Manufacturing Nonmanufacturing Industries Food and Kindred Products (NAICS 311) Metals-Based Durables (NAICS 332-336) Agricultural Production -Crops (NAICS 111) Paper and Allied Products (NAICS 322) Balance of Manufacturing (all remaining manufacturing NAICS) Other Agriculture Including Livestock (NAICS112- 115) Bulk Chemicals (NAICS 32B) Coal Mining (NAICS 2121) Glass and Glass Products (NAICS 3272) Oil and Gas Extraction (NAICS 211) Hydraulic Cement (NAICS 32731) Metal and Other Nonmetallic Mining (NAICS 2122- 2123) Blast Furnaces and Basic Steel (NAICS 331111) Construction (NAICS233-235)

47

EIA - Assumptions to the Annual Energy Outlook 2010 - Petroleum Market  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumptions to the Annual Energy Outlook 2010 Petroleum Market Module The NEMS Petroleum Market Module (PMM) projects petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, unfinished oil imports, other refinery inputs (including alcohols, ethers, bioesters, corn, biomass, and coal), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. Figure 9. Petroleum Administration for Defense Districts. The PMM contains a linear programming (LP) representation of U.S. refining activities in the five Petroleum Area Defense Districts (PADDs) (Figure 9),

48

EIA - Assumptions to the Annual Energy Outlook 2008 - Petroleum Market  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumptions to the Annual Energy Outlook 2008 Petroleum Market Module Figure 9. Petroleum Administration for Defense Districts. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Petroleum Market Module (PMM) projects petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, unfinished oil imports, other refinery inputs (including alcohols, ethers, bioesters, corn, biomass, and coal), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. The PMM contains a linear programming (LP) representation of U.S. refining

49

EIA - Assumptions to the Annual Energy Outlook 2010 - International Energy  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2010 International Energy Module Figure 2. World Oil Prices in Three Cases, 1995-2035 Figure 2. World Oil Prices in three Cases, 1995-2035 (2008 dollars per barrel). Need help, contact the National Energy Information Center at 202-586-8800. figure data Figure 3. OPEC Total Liquids Production in the Reference Case, 1980-2035 Figure 3. OPEC Total Liquids Production in the Reference Case, 1995-2030 (million barrels per day). Need help, contact the National Energy Information Center at 202-586-8800. figure data Figure 4. Non-OPEC Total Liquids Production in the Reference Case, 1980-2035 Figure 4. Non-OPEC Total Liquids Production in the Reference Case, 1995-2030 (million barrels per day). Need help, contact the National Energy Information Center at 202-586-8800.

50

Assumptions to Annual Energy Outlook - Energy Information Administrati...  

Annual Energy Outlook 2012 (EIA)

Assumptions to AEO2013 Release Date: May 14, 2013 | Next Release Date: May 2014 | full report Introduction This report presents the major assumptions of the National Energy...

51

Assumptions to the Annual Energy Outlook - Table 41  

Annual Energy Outlook 2012 (EIA)

> Forecasts >Assumptions to the Annual Energy Outlook> Download Report Assumption to the Annual Energy Outlook Adobe Acrobat Reader Logo Adobe Acrobat Reader is required for PDF...

52

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20031 (AEO2003), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3 The National Energy Modeling System The projections in the AEO2003 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the midterm time period and perform policy analyses requested by decisionmakers and analysts in the U.S. Congress, the Department of EnergyÂ’s Office of Policy and International Affairs, other DOE offices, and other government agencies.

53

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2003, DOE/EIA-M068(2003) April 2003. Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

54

Assumptions to the Annual Energy Outlook - Household Expenditures Module  

Gasoline and Diesel Fuel Update (EIA)

Household Expenditures Module Household Expenditures Module Assumption to the Annual Energy Outlook Household Expenditures Module Figure 5. United States Census Divisions. Having problems, call our National Energy Information Center at 202-586-8800 for help. The Household Expenditures Module (HEM) constructs household energy expenditure profiles using historical survey data on household income, population and demographic characteristics, and consumption and expenditures for fuels for various end-uses. These data are combined with NEMS forecasts of household disposable income, fuel consumption, and fuel expenditures by end-use and household type. The HEM disaggregation algorithm uses these combined results to forecast household fuel consumption and expenditures by income quintile and Census Division (see

55

Assumptions to the Annual Energy Outlook - Contacts  

Gasoline and Diesel Fuel Update (EIA)

Contacts Contacts Assumption to the Annual Energy Outlook Contacts Specific questions about the information in this report may be directed to: Introduction Paul D. Holtberg 202/586-1284 Macroeconomic Activity Module Ronald F. Earley Yvonne Taylor 202/586-1398 202/586-1398 International Energy Module G. Daniel Butler 202/586-9503 Household Expenditures Module/ Residential Demand Module John H. Cymbalsky 202/586-4815 Commercial Demand Module Erin E. Boedecker 202/586-4791 Industrial Demand Module T. Crawford Honeycutt 202/586-1420 Transportation Demand Module John D. Maples 202/586-1757 Electricity Market Module Laura Martin 202/586-1494 Oil and Gas Supply Module/Natural Gas Transmission and Distribution Module Joseph Benneche 202/586-6132 Petroleum Market Module Bill Brown 202/586-8181

56

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Industrial Demand Module Industrial Demand Module This page inTenTionally lefT blank 53 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Industrial Demand Module The NEMS Industrial Demand Module (IDM) estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 non-manufacturing industries. The manufacturing industries are subdivided further into the energy- intensive manufacturing industries and non-energy-intensive manufacturing industries (Table 6.1). The manufacturing industries are modeled through the use of a detailed process-flow or end-use accounting procedure. The non-manufacturing industries are modeled with less detail because processes are simpler and there is less available data. The petroleum refining

57

Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Tools Tools Printable Version Share this resource Send a link to Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology to someone by E-mail Share Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Facebook Tweet about Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Twitter Bookmark Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Google Bookmark Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Delicious Rank Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Digg Find More places to share Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on AddThis.com...

58

Energy Information Administration (EIA) - Assumptions to the...  

Gasoline and Diesel Fuel Update (EIA)

density, housing values, income values, and availability of deepwater ports. The production costs reflect assumed market prices entering the liquefaction facility for...

59

Assumptions to the Annual Energy Outlook 1999 - Table 1  

Gasoline and Diesel Fuel Update (EIA)

Summary of AEO99 Cases Summary of AEO99 Cases Case Name Description Integration mode Reference Baseline economic growth, world oil price, and technology assumptions Fully Integrated Low Economic Growth Gross Domestic product grows at an average annual rate of 1.5 percent, compared to the reference case growth of 2.1 percent. Fully Integrated High Economic Growth Gross domestic product grows at an average annual rate of 2.6 percent, compared to the reference case growth of 2.1 percent. Fully Integrated Low World Oil Price World oil prices are $14.57 per barrel in 2020, compared to $22.73 per barrel in the reference case. Partially Integrated High World Oil Price World oil prices are $29.35 per barrel in 2020, compared to $22.73 per barrel in the reference case. Partially Integrated Residential: 1999 Technology

60

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2006 The International Energy Module determines changes in the world oil price and the supply prices of crude oils and petroleum products for import to the United States in response to changes in U.S. import requirements. A market clearing method is used to determine the price at which worldwide demand for oil is equal to the worldwide supply. The module determines new values for oil production and demand for regions outside the United States, along with a new world oil price that balances supply and demand in the international oil market. A detailed description of the International Energy Module is provided in the EIA publication, Model Documentation Report: The International Energy Module of the National Energy Modeling System, DOE/EIA-M071(06), (Washington, DC, February 2006).

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


61

EIA - Assumptions to the Annual Energy Outlook 2009 - International Energy  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2009 International Energy Module Figure 2. World Oil Prices in three Cases, 1995-2030 (2006 dollars per barrel). Need help, contact the National Energy Information Center at 202-586-8800. figure data Figure 3. OPEC Total Liquids Production in the Reference Case, 1995-2030 (million barrels per day). Need help, contact the National Energy Information Center at 202-586-8800. figure data Figure 4. Non-OPEC Total Liquids Production in the Reference Case, 1995-2030 (million barrels per day). Need help, contact the National Energy Information Center at 202-586-8800. figure data The International Energy Module (IEM) performs two tasks in all NEMS runs. First, the module reads exogenously global and U.S.A. petroleum liquids

62

Assumptions to the Annual Energy Outlook - International Energy Module  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumption to the Annual Energy Outlook International Energy Module Figure 2. World Oil Prices in three Cases, 1970-2025. Having problems, call our National Energy Information Center at 202-586-8800 for help. Figure Data Figure 3. OPEC Oil Production in the Reference Case, 1970-2025. Having problems, call our National Energy Information Center at 202-586-8800 for help. Figure Data Figure 4. Non-OPEC Production in the Reference Case, 1970-2025. Having problems, call our National Energy Information Center at 202-586-8800 for help. Figure Data Table 4. Worldwide Oil Reserves as of January 1, 2002 (Billion Barrels) Printer Friendly Version Region Proved Oil Reserves Western Hemisphere 313.6 Western‘Europe 18.1 Asia-Pacific 38.7

63

EIA - Assumptions to the Annual Energy Outlook 2009 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2009 Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight, rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

64

Assumptions to the Annual Energy Outlook - Transportation Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumption to the Annual Energy Outlook Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, sport utility vehicles and vans), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger airplanes, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

65

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2006 The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption isthe sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

66

EIA - Assumptions to the Annual Energy Outlook 2008 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2008 Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

67

Assumptions to the Annual Energy Outlook 2001 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, industry sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs), freight trucks (>10,000 lbs), freight and passenger airplanes, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption. Key Assumptions Macroeconomic Sector Inputs

68

Energy Information Administration (EIA) - Assumptions to the...  

Gasoline and Diesel Fuel Update (EIA)

Nonfarm labor productivity is expected to diminish from its current high level to a more sustainable level between 1.8 and 2.6 percent for the remainder of the forecast period...

69

Assumptions to the Annual Energy Outlook  

Annual Energy Outlook 2012 (EIA)

in the world oil price and the supply prices of crude oils and petroleum products for import to the United States in response to changes in U.S. import requirements. A market...

70

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 9 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 20). The Industrial Demand Module forecasts energy consumption at the four Census region levels; energy consumption at the Census Division level is allocated by using the SEDS24 data. The energy-intensive industries (food and kindred products, paper and allied products, bulk chemicals, glass and glass products, hydraulic cement, blast furnace and basic steel products, and aluminum) are modeled in considerable detail. Each industry is modeled as three separate but interrelated components consisting of the Process Assembly (PA) Component, the Buildings Component (BLD), and the Boiler/Steam/Cogenera- tion (BSC) Component. The BSC Component satisfies the steam demand from the PA and BLD Components. In some industries, the PA Component produces byproducts that are consumed in the BSC Component. For the manufacturing industries, the PA Component is separated into the major production processes or end uses.

71

Abstract„Production of two types of superconducting  

NLE Websites -- All DOE Office Websites (Extended Search)

68271-AB 68271-AB Abstract submitted to the 17 th International Conf. on Magnet Technology, Geneva, Sept. 24-28, 2001 Test Results for Prototypes of the Twin Aperture Dipoles for the LHC Insertion Region* J. Muratore, M. Anerella, J. Cozzolino, G. Ganetis, A. Ghosh, R. Gupta, M. Harrison, A. Jain, A. Marone, S. Plate, J. Schmalzle, R. Thomas, P. Wanderer, E. Willen and K.C. Wu Brookhaven National Laboratory, P.O. Box 5000, Upton, NY 11973-5000 Abstract-The Superconducting Magnet Division at Brookhaven National Laboratory (BNL) is building 26 insertion region dipoles of various types for the Large Hadron Collider (LHC) at CERN. These 9.45 m-long, 8 cm aperture magnets use the same coil design as the arc dipoles for the Relativistic Heavy Ion Collider (RHIC) at BNL. The

72

Assumptions to the Annual Energy Outlook 2000 - Natural Gas Transmission  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution network that links them. In addition, natural gas flow patterns are a function of the pattern in the previous year, coupled with the relative prices of gas supply options as translated to the represented market “hubs.” The major assumptions used within the NGTDM are grouped into five general categories. They relate to (1) the classification of demand into core and noncore transportation service classes, (2) the pricing of transmission and distribution services, (3) pipeline and storage capacity expansion and utilization, (4) the implementation of recent regulatory reform, and (5) the implementation of provisions of the Climate Change Action Plan (CCAP). A complete listing of NGTDM assumptions and in-depth methodology descriptions are presented in Model Documentation: Natural Gas Transmission and Distribution Model of the National Energy Modeling System, Model Documentation 2000, DOE/EIA-M062(2000), January 2000.

73

Assumptions to the Annual Energy Outlook 1999 - Natural Gas Transmission  

Gasoline and Diesel Fuel Update (EIA)

The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by obtaining market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution network that links them. In addition, natural gas flow patterns are a function of the pattern in the previous year, coupled with the relative prices of gas supply options as translated to the represented market “hubs.” The major assumptions used within the NGTDM are grouped into five general categories. They relate to (1) the classification of demand into core and noncore transportation service classes, (2) the pricing of transmission and distribution services, (3) pipeline and storage capacity expansion and utilization, (4) the implementation of recent regulatory reform, and (5) the implementation of provisions of the Climate Change Action Plan (CCAP). A complete listing of NGTDM assumptions and in-depth methodology descriptions are presented in Model Documentation Report: Natural Gas Transmission and Distribution Model of the National Energy Modeling System, DOE/EIA-MO62/1, January 1999.

74

An Assessment of Reactor Types for Thermochemical Hydrogen Production  

DOE Green Energy (OSTI)

Nuclear energy has been proposed as a heat source for producing hydrogen from water using a sulfur-iodine thermochemical cycle. This document presents an assessment of the suitability of various reactor types for this application. The basic requirement for the reactor is the delivery of 900 C heat to a process interface heat exchanger. Ideally, the reactor heat source should not in itself present any significant design, safety, operational, or economic issues. This study found that Pressurized and Boiling Water Reactors, Organic-Cooled Reactors, and Gas-Core Reactors were unsuitable for the intended application. Although Alkali Metal-Cooled and Liquid-Core Reactors are possible candidates, they present significant development risks for the required conditions. Heavy Metal-Cooled Reactors and Molten Salt-Cooled Reactors have the potential to meet requirements, however, the cost and time required for their development may be appreciable. Gas-Cooled Reactors (GCRs) have been successfully operated in the required 900 C coolant temperature range, and do not present any obvious design, safety, operational, or economic issues. Altogether, the GCRs approach appears to be very well suited as a heat source for the intended application, and no major development work is identified. This study recommends using the Gas-Cooled Reactor as the baseline reactor concept for a sulfur-iodine cycle for hydrogen generation.

MARSHALL, ALBERT C.

2002-02-01T23:59:59.000Z

75

EIA - Assumptions to the Annual Energy Outlook 2008 - International Energy  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2008 International Energy Module The International Energy Module (IEM) performs two tasks in all NEMS runs. First, the module reads exogenously global and U.S.A. petroleum liquids supply and demand curves (1 curve per year; 2008-2030; approximated, isoelastic fit to previous NEMS results). These quantities are not modeled directly in NEMS. Previous versions of the IEM adjusted these quantities after reading in initial values. In an attempt to more closely integrate the AEO2008 with IEO2007 and the STEO some functionality was removed from IEM while a new algorithm was implemented. Based on the difference between U.S. total petroleum liquids production (consumption) and the expected U.S. total liquids production (consumption) at the current WTI price, curves for global petroleum liquids consumption (production) were adjusted for each year. According to previous operations, a new WTI price path was generated. An exogenous oil supply module, Generate World Oil Balances (GWOB), was also used in IEM to provide annual regional (country) level production detail for conventional and unconventional liquids.

76

Assumptions to the Annual Energy Outlook 2000 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module estimates energy consumption across the nine Census Divisions and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, industry sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs), freight trucks (>10,000 lbs), freight and passenger airplanes, freight rail, freight shipping, mass transit, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption. Transportation Demand Module estimates energy consumption across the nine Census Divisions and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, industry sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs), freight trucks (>10,000 lbs), freight and passenger airplanes, freight rail, freight shipping, mass transit, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption. Key Assumptions Macroeconomic Sector Inputs

77

EIA - Assumptions to the Annual Energy Outlook 2009 - Renewable Fuels  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module Assumptions to the Annual Energy Outlook 2009 Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for projections of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind1. Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was one of the first electric generation technologies, to newer power systems using biomass, geothermal, LFG, solar, and wind energy.

78

EIA - Assumptions to the Annual Energy Outlook 2010 - Natural Gas  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumptions to the Annual Energy Outlook 2010 Natural Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and distribution Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each projection year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and

79

EIA - Assumptions to the Annual Energy Outlook 2008 - Natural Gas  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumptions to the Annual Energy Outlook 2008 Natural Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and Distribution Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each projection year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution

80

EIA-Assumptions to the Annual Energy Outlook - Macroeconomic Activity  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module Assumptions to the Annual Energy Outlook 2007 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2007), (Washington, DC, January 2007).

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


81

EIA - Assumptions to the Annual Energy Outlook 2009 - Macroeconomic  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module Assumptions to the Annual Energy Outlook 2010 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Document>ation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2009), (Washington, DC, January 2009).

82

EIA - Assumptions to the Annual Energy Outlook 2009 - Natural Gas  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumptions to the Annual Energy Outlook 2009 Natural Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and distribution Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each projection year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution

83

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7), 7), (Washington, DC, January 2007). Key Assumptions The output of the U.S. economy, measured by GDP, is expected to increase by 2.9 percent between 2005 and 2030 in the reference case. Two key factors help explain the growth in GDP: the growth rate of nonfarm employment and the rate of productivity change associated with employment. As Table 3 indicates, for the Reference Case GDP growth slows down in each of the periods identified, from 3.0 percent between 2005 and 2010, to 2.9 percent between 2010 and 2020, to 2.8 percent in the between 2020 and 2030. In the near term from 2005 through 2010, the growth in nonfarm employment is low at 1.2 percent compared with 2.4 percent in the second half of the 1990s, while the economy is expected to experiencing relatively strong

84

Assumptions to the Annual Energy Outlook - Natural Gas Transmission and  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumption to the Annual Energy Outlook Natural Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and Distribution Model Regions. Having problems, call our National Energy Information Center at 202-586-8800 for help. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution

85

EIA - Assumptions to the Annual Energy Outlook 2008 - Macroeconomic  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module Assumptions to the Annual Energy Outlook 2008 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2007), (Washington, DC, January 2007).

86

EIA - Assumptions to the Annual Energy Outlook 2009 - Macroeconomic  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module Assumptions to the Annual Energy Outlook 2009 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2008), (Washington, DC, January 2008).

87

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger airplanes, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

88

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimates of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and Census Division and prices for each energy source for each of the nine Census Divisions. The Residential Demand Module also requires projections of available equipment over the forecast horizon. Over time, equipment efficiency tends to increase because of general technological advances and also because of Federal and/or state efficiency standards. As energy prices and available equipment changes over the forecast horizon, the module includes projected changes to the type and efficiency of equipment purchased as well as projected changes in the usage intensity of the equipment stock.

89

EIA - Assumptions to the Annual Energy Outlook 2009 - Commercial Demand  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2009 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2030. The definition of the commercial sector is consistent with EIAÂ’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.1

90

EIA - Assumptions to the Annual Energy Outlook 2010 - Commercial Demand  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2009 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2035. The definition of the commercial sector is consistent with EIAÂ’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services [1].

91

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2006 The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2030. The definition of the commercial sector is consistent with EIAÂ’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.14

92

EIA - Assumptions to the Annual Energy Outlook 2010 - Residential Demand  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2010 Residential Demand Module Figure 5. United States Census Divisions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Residential Demand Module projects future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the "unit energy consumption" by appliance (or UEC-in million Btu per household per year). The projection process adds new housing units to the stock,

93

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2006 Figure 5. United States Census Divisions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment

94

EIA - Assumptions to the Annual Energy Outlook 2008 - Commercial Demand  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2008 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2030. The definition of the commercial sector is consistent with EIAÂ’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.1

95

Production of CO2 from Fossil Fuel Burning by Fuel Type, 1860...  

NLE Websites -- All DOE Office Websites (Extended Search)

Fossil Fuel CO2 Emissions Historical Global Estimates Production of CO2 from Fossil Fuel Burning by Fuel Type, 1860-1982 (NDP-006) DOI: 10.3334CDIACffe.ndp006 image Data image...

96

U.S. Product Supplied of Kerosene-Type Jet Fuel (Thousand Barrels)  

U.S. Energy Information Administration (EIA)

U.S. Product Supplied of Kerosene-Type Jet Fuel (Thousand Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9; 1980's: 295,460 ...

97

Multiple-part-type systems in high volume manufacturing : Kanban System design for automatic production scheduling  

E-Print Network (OSTI)

A Kanban Production System is designed to help a factory line meet fluctuating demands for multiple part types. Based on the parameter settings of the Control-Point Policy, the optimum Kanban levels are obtained. The ...

Lee, Kaizhao

2008-01-01T23:59:59.000Z

98

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2003), (Washington, DC, January 2003).

99

Assumptions to the Annual Energy Outlook - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumption to the Annual Energy Outlook Residential Demand Module The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimates of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and Census Division and prices for each energy source for each of the nine Census Divisions (see Figure 5). The Residential Demand Module also requires projections of available equipment and their installed costs over the forecast horizon. Over time, equipment efficiency tends to increase because of general technological advances and also because of Federal and/or state efficiency standards. As energy prices and available equipment changes over the forecast horizon, the module includes projected changes to the type and efficiency of equipment purchased as well as projected changes in the usage intensity of the equipment stock.

100

EIA - Assumptions to the Annual Energy Outlook 2009 - Residential Demand  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2009 Residential Demand Module The NEMS Residential Demand Module projects future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and Census Division and prices for each energy source for each of the nine Census Divisions (see Figure 5). The Residential Demand Module also requires projections of available equipment and their installed costs over the projection horizon. Over time, equipment efficiency tends to increase because of general technological advances and also because of Federal and/or state efficiency standards. As energy prices and available equipment changes over the projection horizon, the module includes projected changes to the type and efficiency of equipment purchased as well as projected changes in the usage intensity of the equipment stock.

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


101

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has five submodules representing various renewable energy sources, biomass, geothermal, landfill gas, solar, and wind; a sixth renewable, conventional hydroelectric power, is represented in the Electricity Market Module (EMM).119 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was an original source of electricity generation, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon low-cost energy storage.

102

EIA - Assumptions to the Annual Energy Outlook 2010  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to the Annual Energy Outlook 2010 This report summarizes the major assumptions used in the NEMS to generate the AEO2010 projections. Introduction Macroeconomic Activity Module International Energy Module Residential Demand Module Commercial Demand Module Industrial Demand Module Transportation Demand Module Electricity Market Module Oil and Gas Supply Module Natural Gas Transmission and Distribution Module Petroleum Market Module Coal Market Module Renewable Fuels Module PDF (GIF) Appendix A: Handling of Federal and Selected State Legislation and Regulation In the Annual Energy Outlook Past Assumptions Editions Download the Report Assumptions to the Annual Energy Outlook 2010 Report Cover. Need help, contact the National Energy Information Center at 202-586-8800.

103

Assumptions to the Annual Energy Outlook - Macroeconomic Activity...  

Annual Energy Outlook 2012 (EIA)

Macroeconomic Activity Module Assumption to the Annual Energy Outlook Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the...

104

Assumption-Commitment Support for CSP Model Checking  

E-Print Network (OSTI)

AVoCS 2006 Assumption-Commitment Support for CSP Model Checking Nick Moffat1 Systems Assurance using CSP. In our formulation, an assumption-commitment style property of a process SYS takes the form-Guarantee, CSP, Model Checking, Compositional Reasoning 1 Introduction The principle of compositional program

Paris-Sud XI, Université de

105

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

6 6 Assumptions to the Annual Energy Outlook 2006 This report presents major assumptions of NEMS that are used to generate the projections in the AEO2006. Contents (Complete Report) Download complete Report. Need help, contact the National Energy Information Center at 202-586-8800. Introduction Introduction Section to the Assumptions to the Annual Energy Outlook 2006 Report. Need help, contact the National Energy Information Center at 202-586-8800. Introduction Section to the Assumptions to the Annual Energy Outlook 2006 Report. Need help, contact the National Energy Information Center at 202-586-8800. Macroeconomic Activity Module Macroeconomic Activity Module Section to the Assumptions to the Annual Energy Outlook 2006 Report. Need help, contact the National Energy Information Center at 202-586-8800.

106

Assumptions to the Annual Energy Outlook - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module Assumption to the Annual Energy Outlook Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has five submodules representing various renewable energy sources, biomass, geothermal, landfill gas, solar, and wind; a sixth renewable, conventional hydroelectric power, is represented in the Electricity Market Module (EMM).109 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was an original source of electricity generation, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon low-cost energy storage.

107

EIA - Assumptions to the Annual Energy Outlook 2008 - Renewable Fuels  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module Assumptions to the Annual Energy Outlook 2008 Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for projections of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind1. Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was one of the first electric generation technologies, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon the availability of low-cost energy storage systems.

108

EIA - Assumptions to the Annual Energy Outlook 2009  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to the Annual Energy Outlook 2009 The Early Release for next year's Annual Energy Outlook will be presented at the John Hopkins Kenney Auditorium on December 14th This report summarizes the major assumptions used in the NEMS to generate the AEO2009 projections. Introduction Macroeconomic Activity Module International Energy Module Residential Demand Module Commercial Demand Module Industrial Demand Module Transportation Demand Module Electricity Market Module Oil and Gas Supply Module Natural Gas Transmission and Distribution Module Petroleum Market Module Coal Market Module Renewable Fuels Module PDF (GIF) Appendix A: Handling of Federal and Selected State Legislation and Regulation In the Annual Energy Outlook Past Assumptions Editions

109

AEO2011: Coal Production by Region and Type | OpenEI  

Open Energy Info (EERE)

by Region and Type by Region and Type Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is Table 140, and contains only the reference case. The unit of measurement in this dataset is million short tons. The data is broken down into northern Appalachia, central Appalachia, southern Appalachia, eastern interior, western interior, gulf, Dakota medium, western montana, Wyoming, Rocky Mountain, Arizona/New Mexico and Washington/Alaska. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO Coal Production EIA Data application/vnd.ms-excel icon AE2011: Coal Production by Region and Type- Reference Case (xls, 122.3 KiB)

110

Assumptions to Annual Energy Outlook - Energy Information Administration  

U.S. Energy Information Administration (EIA) Indexed Site

Assumptions to AEO2013 Assumptions to AEO2013 Release Date: May 14, 2013 | Next Release Date: May 2014 | full report Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2013 [1] (AEO2013), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports [2]. The National Energy Modeling System Projections in the AEO2013 are generated using the NEMS, developed and maintained by the Office of Energy Analysis of the U.S. Energy Information Administration (EIA). In addition to its use in developing the Annual

111

Assumptions to Annual Energy Outlook - Energy Information Administration  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to AEO2012 Assumptions to AEO2012 Release Date: August 2, 2012 | Next Release Date: August 2013 | Full report Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2012 [1] (AEO2012), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports [2]. The National Energy Modeling System The projections in AEO2012 are generated using the NEMS, developed and maintained by the Office of Energy Analysis (OEA) of the U.S. Energy Information Administration (EIA). In addition to its use in developing the

112

Notes 01. The fundamental assumptions and equations of lubrication theory  

E-Print Network (OSTI)

The fundamental assumption in Lubrication Theory. Derivation of thin film flow equations from Navier-Stokes equations. Importance of fluid inertia effects in thin film flows. Some fluid physical properties

San Andres, Luis

2009-01-01T23:59:59.000Z

113

Computational soundness for standard assumptions of formal cryptography  

E-Print Network (OSTI)

This implementation is conceptually simple, and relies only on general assumptions. Specifically, it can be thought of as a 'self-referential' variation on a well-known encryption scheme. 4. Lastly, we show how the ...

Herzog, Jonathan, 1975-

2004-01-01T23:59:59.000Z

114

LBL-34045 UC-1600 Residential HVAC Data, Assumptions and Methodology  

NLE Websites -- All DOE Office Websites (Extended Search)

5 UC-1600 Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1 Francis X. Johnson, Richard E. Brown, James W. Hanford, Alan H. Sanstad and...

115

Assumptions to the Annual Energy Outlook 1999 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

bullet1.gif (843 bytes) Feedback link.gif (1946 bytes) bullet1.gif (843 bytes) Assumptions to the AEO99 bullet1.gif (843 bytes) Interactive Data Queries to the AEO99 bullet1.gif...

116

Idaho National Engineering Laboratory installation roadmap assumptions document. Revision 1  

SciTech Connect

This document is a composite of roadmap assumptions developed for the Idaho National Engineering Laboratory (INEL) by the US Department of Energy Idaho Field Office and subcontractor personnel as a key element in the implementation of the Roadmap Methodology for the INEL Site. The development and identification of these assumptions in an important factor in planning basis development and establishes the planning baseline for all subsequent roadmap analysis at the INEL.

Not Available

1993-05-01T23:59:59.000Z

117

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2006 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20061 (AEO2006), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview3, which is updated once every few years. The National Energy Modeling System

118

Assumptions to the Annual Energy Outlook 1999 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

link.gif (1946 bytes) link.gif (1946 bytes) bullet1.gif (843 bytes) Assumptions to the AEO99 bullet1.gif (843 bytes) Supplemental Tables to the AEO99 bullet1.gif (843 bytes) To Forecasting Home Page bullet1.gif (843 bytes) EIA Homepage introduction.gif (4117 bytes) This paper presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 19991 (AEO99), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3

119

,"Crude Oil and Petroleum Products Total Stocks Stocks by Type"  

U.S. Energy Information Administration (EIA) Indexed Site

Total Stocks Stocks by Type" Total Stocks Stocks by Type" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Crude Oil and Petroleum Products Total Stocks Stocks by Type",6,"Monthly","9/2013","1/15/1956" ,"Release Date:","11/27/2013" ,"Next Release Date:","Last Week of December 2013" ,"Excel File Name:","pet_stoc_typ_a_ep00_sae_mbbl_m.xls" ,"Available from Web Page:","http://www.eia.gov/dnav/pet/pet_stoc_typ_a_ep00_sae_mbbl_m.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.gov"

120

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

States. States. OGSM encompasses domestic crude oil and natural gas supply by both conventional and nonconventional recovery techniques. Nonconventional recovery includes unconventional gas recovery from low permeability formations of sandstone and shale, and coalbeds. Energy Information Administration/Assumptions to the Annual Energy Outlook 2007 93 Figure 7. Oil and Gas Supply Model Regions Source: Energy Information Administration, Office of Integrated Analysis and Forecasting. Report #:DOE/EIA-0554(2007) Release date: April 2007 Next release date: March 2008 Primary inputs for the module are varied. One set of key assumptions concerns estimates of domestic technically recoverable oil and gas resources. Other factors affecting the projection include the assumed

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


121

EIA-Assumptions to the Annual Energy Outlook - International Energy Module  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2007 International Energy Module The International Energy Module (IEM) performs two tasks in all NEMS runs. First, the module reads exogenously derived supply curves, initial price paths and international regional supply and demand levels into NEMS. These quantities are not modeled directly in NEMS because NEMS is not an international model. Previous versions of the IEM adjusted these quantities after reading in initial values. In an attempt to more closely integrate the AEO2007 with the IEO2006 and the STEO some functionality was removed from the IEM. More analyst time was devoted to analyzing price relationships between marker crude oils and refined products. A new exogenous oil supply model, Generate World Oil Balances (GWOB), was also developed to incorporate actual investment occurring in the international oil market through 2015 and resource assumptions through 2030. The GWOB model provides annual country level oil production detail for eight conventional and unconventional oils.

122

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7) 7) Release date: April 2007 Next release date: March 2008 Assumptions to the Annual Energy Outlook 2007 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Commercial Demand Module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Natural Gas Transmission and Distribution Module. . . . . . . . . . . . . . . . . . . . . . 107 Petroleum Market Module

123

A Comparison of the Free Ride and CISK Assumptions  

Science Conference Proceedings (OSTI)

In a recent paper Fraedrich and McBride have studied the relation between the “free ride” and CISK (conditional instability of the second kind) assumptions in a well-known two-layer model. Here the comparison is extended to a more general case. ...

Torben Strunge Pedersen

1991-08-01T23:59:59.000Z

124

PROJECT MANGEMENT PLAN EXAMPLES Policy & Operational Decisions, Assumptions  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Policy & Operational Decisions, Assumptions Policy & Operational Decisions, Assumptions and Strategies Examples 1 & 2 Example 1 1.0 Summary The 322-M Metallurgical Laboratory is currently categorized as a Radiological Facility. It is inactive with no future DOE mission. In May of 1998 it was ranked Number 45 in the Inactive Facilities Risk Ranking database which the Facilities Decommissioning Division maintains. A short-term surveillance and maintenance program is in-place while the facility awaits final deactivation. Completion of the end points described in this deactivation project plan will place the 322-M facility into an End State that can be described as "cold and dark". The facility will be made passively safe requiring minimal surveillance and no scheduled maintenance.

125

Cost and Performance Assumptions for Modeling Electricity Generation Technologies  

NLE Websites -- All DOE Office Websites (Extended Search)

Cost and Performance Cost and Performance Assumptions for Modeling Electricity Generation Technologies Rick Tidball, Joel Bluestein, Nick Rodriguez, and Stu Knoke ICF International Fairfax, Virginia Subcontract Report NREL/SR-6A20-48595 November 2010 NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC. National Renewable Energy Laboratory 1617 Cole Boulevard Golden, Colorado 80401 303-275-3000 * www.nrel.gov Contract No. DE-AC36-08GO28308 Cost and Performance Assumptions for Modeling Electricity Generation Technologies Rick Tidball, Joel Bluestein, Nick Rodriguez, and Stu Knoke ICF International Fairfax, Virginia NREL Technical Monitor: Jordan Macknick

126

Assumptions to the Annual Energy Outlook 2002 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20021 (AEO2002), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3 The National Energy Modeling System The projections in the AEO2002 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of

127

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7, DOE/EIA- 7, DOE/EIA- M068(2007). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described. EMM Regions The supply regions used in EMM are based on the North American Electric Reliability Council regions and

128

Assumptions to the Annual Energy Outlook 2001 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Outlook2001 Outlook2001 Supplemental Data to the AEO2001 NEMS Conference To Forecasting Home Page EIA Homepage Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20011 (AEO2001), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3 The National Energy Modeling System The projections in the AEO2001 were produced with the National Energy

129

Assumptions to the Annual Energy Outlook 2000 - Errata  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to the Annual Energy Outlook 2000 Assumptions to the Annual Energy Outlook 2000 as of 4/4/2000 1. On table 20 "the fractional fuel efficiency change for 4-Speed Automatic" should be .045 instead of .030. On table 20 "the fractional fuel efficiency change for 5-Speed Automatic" should be .065 instead of .045. (Change made on 3/6/2000) 2. Table 28 should be labeled: "Alternative-Fuel Vehicle Attribute Inputs for Compact Cars for Two Stage Logit Model". (Change made on 3/6/2000) 3. The capital costs in Table 29 should read 1998 dollars not 1988 dollars. (Change made on 3/6/2000) 4. Table 37 changed the label "Year Available" to "First Year Completed." Changed the second sentence of Footnote 1 to read "these estimates are costs of new projects

130

Assumptions to the Annual Energy Outlook 2000 - Household Expenditures  

Gasoline and Diesel Fuel Update (EIA)

Key Assumptions Key Assumptions The historical input data used to develop the HEM version for the AEO2000 consists of recent household survey responses, aggregated to the desired level of detail. Two surveys performed by the Energy Information Administration are included in the AEO2000 HEM database, and together these input data are used to develop a set of baseline household consumption profiles for the direct fuel expenditure analysis. These surveys are the 1997 Residential Energy Consumption Survey (RECS) and the 1991 Residential Transportation Energy Consumption Survey (RTECS). HEM uses the consumption forecast by NEMS for the residential and transportation sectors as inputs to the disaggregation algorithm that results in the direct fuel expenditure analysis. Household end-use and personal transportation service consumption are obtained by HEM from the NEMS Residential and Transportation Demand Modules. Household disposable income is adjusted with forecasts of total disposable income from the NEMS Macroeconomic Activity Module.

131

Effects of internal gain assumptions in building energy calculations  

DOE Green Energy (OSTI)

The utilization of direct solar gains in buildings can be affected by operating profiles, such as schedules for internal gains, thermostat controls, and ventilation rates. Building energy analysis methods use various assumptions about these profiles. The effects of typical internal gain assumptions in energy calculations are described. Heating and cooling loads from simulations using the DOE 2.1 computer code are compared for various internal-gain inputs: typical hourly profiles, constant average profiles, and zero gain profiles. Prototype single-family-detached and multi-family-attached residential units are studied with various levels of insulation and infiltration. Small detached commercial buildings and attached zones in large commercial buildings are studied with various levels of internal gains. The results of this study indicate that calculations of annual heating and cooling loads are sensitive to internal gains, but in most cases are relatively insensitive to hourly variations in internal gains.

Christensen, C.; Perkins, R.

1981-01-01T23:59:59.000Z

132

EIA - Assumptions to the Annual Energy Outlook 2009 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2009 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2009 (AEO2009),1 including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 The National Energy Modeling System The projections in the AEO2009 were produced with the NEMS, which is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and perform policy analyses requested by decisionmakers in the White House, U.S. Congress, offices within the Department of Energy, including DOE Program Offices, and other government agencies. The Annual Energy Outlook (AEO) projections are also used by analysts and planners in other government agencies and outside organizations.

133

Assumptions to the Annual Energy Outlook - Electricity Market Module  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumption to the Annual Energy Outlook Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2004, DOE/EIA- M068(2004). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

134

EIA - Assumptions to the Annual Energy Outlook 2010 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2010 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2010 [1] (AEO2010), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports [2]. The National Energy Modeling System The projections in the AEO2010 were produced with the NEMS, which is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and perform policy analyses requested by decisionmakers in the White House, U.S. Congress, offices within the Department of Energy, including DOE Program Offices, and other government agencies. The Annual Energy Outlook (AEO) projections are also used by analysts and planners in other government agencies and outside organizations.

135

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumptions to the Annual Energy Outlook 2006 The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand electricity, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2006, DOE/EIA- M068(2006). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

136

EIA - Assumptions to the Annual Energy Outlook 2008 - Electricity Market  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumptions to the Annual Energy Outlook 2008 Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand electricity, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2008, DOE/EIA-M068(2008). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

137

EIA - Assumptions to the Annual Energy Outlook 2008 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2008 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20081 (AEO2008), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 The National Energy Modeling System The projections in the AEO2008 were produced with the NEMS, which is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and perform policy analyses requested by decisionmakers in the White House, U.S. Congress, offices within the Department of Energy, including DOE Program Offices, and other government agencies. The AEO projections are also used by analysts and planners in other government agencies and outside organizations.

138

Molecular Typing and Antimicrobial Resistance of Campylobacter Isolated During Commercial Broiler Production  

E-Print Network (OSTI)

Campylobacter jejuni is a commensal microorganism of the poultry gastrointestinal tract. Broilers, layers, ducks, turkeys, and quails can be colonized by Campylobacter without illness occurring. The vast majority of human Campylobacter infections are recognized as being foodborne. For 2008, preliminary FoodNet data showed that the Campylobacter incidence of infection, 12.68 per 100,000 of the U.S. population, is the second highest, only behind Salmonella at 16.20 per 100,000. To further understand Campylobacter’s role as a foodborne pathogen, analysis at the molecular level is needed. Microbial molecular typing allows for identification and differentiation of bacterial strains beneath the species level. In this study, the “gold standard” method for molecular subtyping, Pulsed Field Gel Electrophoresis (PFGE), along with Diversilab® repetitive element Polymerase Chain Reaction (rep-PCR) and 16S-23S Internal Spacer Region Denaturing Gradient Gel Electrophoresis (ISR DGGE) were used for the molecular typing of Campylobacter jejuni isolates obtained during different stages of commercial broiler production and processing. In addition, the C. jejuni isolates were tested for resistance to antimicrobials commonly used in both veterinary and human medicine. Antimicrobial resistance testing was carried out using a broth dilution system. The majority of recovered isolates came from post-harvest carcass rinsates. Carcass rinses were obtained at post-evisceration, post-chill stages. All isolates (n = 46) were identified by the Polymerase Chain Reaction as Campylobacter jejuni. Three genotypes (n = 44, n = 1, n = 1) were identified by PFGE. The 46 rep-PCR products grouped into seven clusters and two outliers. Clustering of rep-PCR products by sample source was not observed. No relatedness trends were observed for isolates recovered from the same source. The combination of PFGE and Diversilab rep-PCR methods provides highly discriminatory molecular typing results. These results provide practical epidemiological information that shows postevisceration and post-chill stages are still important targets for intervention studies. The very high occurrence of C. jejuni isolates exhibiting genotype A suggests it may differentially express certain gene(s) that enable this strain to more favorably survive under the different harsh environmental conditions encountered during production and processing. In addition, phenotypic testing revealed all of the isolates were not resistant to the antimicrobials azithromycin, ciprofloxacin, erythromycin, gentamycin, tetracycline, florfenicol, nalidixic acid, telithromycin, and clindamycin at any of the concentrations tested. All the C. jejuni isolates exhibited an indistinguishable two-band 16S-23S ISR DGGE profile. Overall, these C. jejuni commercial broiler pre- and post-harvest isolates exhibited an extremely low degree of molecular and phenotypic variability.

Hernandez, Charles Andrew

2010-12-01T23:59:59.000Z

139

U.S. Crude Oil and Petroleum Products Stocks by Type  

U.S. Energy Information Administration (EIA) Indexed Site

Product: Crude Oil and Petroleum Products Crude Oil All Oils (Excluding Crude Oil) Pentanes Plus Liquefied Petroleum Gases Ethane/Ethylene Ethylene Propane/Propylene Propylene (Nonfuel Use) Normal Butane/Butylene Refinery Grade Butane Isobutane/Butylene Other Hydrocarbons Oxygenates (excluding Fuel Ethanol) MTBE Other Oxygenates Renewables (including Fuel Ethanol) Fuel Ethanol Renewable Diesel Fuel Other Renewable Fuels Unfinished Oils Unfinished Oils, Naphthas & Lighter Unfinished Oils, Kerosene & Light Gas Unfinished Oils, Heavy Gas Oils Residuum Motor Gasoline Blending Comp. (MGBC) MGBC - Reformulated MGBC - Reformulated, RBOB MGBC - Reformulated, RBOB w/ Alcohol MGBC - Reformulated, RBOB w/ Ether MGBC - Reformulated, GTAB MGBC - Conventional MGBC - Conventional, CBOB MGBC - Conventional, GTAB MGBC - Conventional Other Aviation Gasoline Blending Comp. Finished Motor Gasoline Reformulated Gasoline Reformulated Gasoline Blended w/ Fuel Ethanol Reformulated Gasoline, Other Conventional Gasoline Conventional Gasoline Blended Fuel Ethanol Conventional Gasoline Blended Fuel Ethanol, Ed55 and Lower Conventional Other Gasoline Finished Aviation Gasoline Kerosene-Type Jet Fuel Kerosene Distillate Fuel Oil Distillate F.O., 15 ppm Sulfur and under Distillate F.O., Greater than 15 to 500 ppm Sulfur Distillate F.O., Greater 500 ppm Sulfur Residual Fuel Oil Residual F.O., than 1.00% Sulfur Petrochemical Feedstocks Naphtha for Petro. Feedstock Use Other Oils for Petro. Feedstock Use Special Naphthas Lubricants Waxes Petroleum Coke Asphalt and Road Oil Miscellaneous Products

140

EIA-Assumptions to the Annual Energy Outlook - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumptions to the Annual Energy Outlook 2007 Petroleum Market Module Figure 9. Petroleum Administration for Defense Districts. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, unfinished oil imports, other refinery inputs (including alcohols, ethers, and bioesters), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. The PMM contains a linear programming (LP) representation of U.S. refining

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


141

Assumptions to the Annual Energy Outlook 2000 - Electricity Market Demand  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module (EMM) represents the planning, operations, and pricing of electricity in the United States. It is composed of four primary submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. In addition, nonutility generation and supply and electricity transmission and trade are represented in the planning and dispatching submodules. Electricity Market Module (EMM) represents the planning, operations, and pricing of electricity in the United States. It is composed of four primary submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. In addition, nonutility generation and supply and electricity transmission and trade are represented in the planning and dispatching submodules. Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. The major assumptions are summarized below.

142

Assumptions to the Annual Energy Outlook 2000 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction This paper presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20001 (AEO2000), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3 The National Energy Modeling System The projections in the AEO2000 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the midterm time period and perform policy analyses requested by decisionmakers and analysts in the U.S. Congress, the Department of EnergyÂ’s Office of Policy, other DOE offices, and other government agencies.

143

Bases, Assumptions, and Results of the Flowsheet Calculations for the Decision Phase Salt Disposition Alternatives  

SciTech Connect

The High Level Waste (HLW) Salt Disposition Systems Engineering Team was formed on March 13, 1998, and chartered to identify options, evaluate alternatives, and recommend a selected alternative(s) for processing HLW salt to a permitted wasteform. This requirement arises because the existing In-Tank Precipitation process at the Savannah River Site, as currently configured, cannot simultaneously meet the HLW production and Authorization Basis safety requirements. This engineering study was performed in four phases. This document provides the technical bases, assumptions, and results of this engineering study.

Dimenna, R.A.; Jacobs, R.A.; Taylor, G.A.; Durate, O.E.; Paul, P.K.; Elder, H.H.; Pike, J.A.; Fowler, J.R.; Rutland, P.L.; Gregory, M.V.; Smith III, F.G.; Hang, T.; Subosits, S.G.; Campbell, S.G.

2001-03-26T23:59:59.000Z

144

Assumptions to the Annual Energy Outlook 2001 - Table 3. Coal-Related  

Gasoline and Diesel Fuel Update (EIA)

Coal-Related Methane Assumptions Coal-Related Methane Assumptions Northern Appalachia Central Appalachia Southern Appalachia Eastern Interior Western Fraction of underground coal production at: Gassy mines 0.885 0.368 0.971 0.876 0.681 Nongassy mines 0.115 0.632 0.029 0.124 0.319 Production from mines with degasification systems (fraction of underground production) 0.541 0.074 0.810 0.067 0.056 Emission factors (kilograms methane per short ton of coal produced) Underground Mining Gassy mines 6.047 5.641 27.346 2.988 6.027 Nongassy mines 0.362 0.076 15.959 0.285 0.245 Degassified mines 4.085 37.724 22.025 0.310 0.000 Surface Mining 0.706 0.706 0.706 0.706 0.706 Post-Mining, underground-mined 1.505 1.505 1.505 1.505 1.505 Post-Mining, surface-mined 0.061 0.061 0.061 0.061 0.061 Methane recovery at active coal mines

145

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

clothes drying, ceiling fans, coffee makers, spas, home security clothes drying, ceiling fans, coffee makers, spas, home security systems, microwave ovens, set-top boxes, home audio equipment, rechargeable electronics, and VCR/DVDs. In addition to the major equipment-driven end-uses, the average energy consumption per household is projected for other electric and nonelectric appliances. The module's output includes number Energy Information Administration/Assumptions to the Annual Energy Outlook 2007 19 Pacific East South Central South Atlantic Middle Atlantic New England West South Central West North Central East North Central Mountain AK WA MT WY ID NV UT CO AZ NM TX OK IA KS MO IL IN KY TN MS AL FL GA SC NC WV PA NJ MD DE NY CT VT ME RI MA NH VA WI MI OH NE SD MN ND AR LA OR CA HI Middle Atlantic New England East North Central West North Central Pacific West South Central East South Central

146

EIA - Assumptions to the Annual Energy Outlook 2009 - Industrial Demand  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2009 Industrial Demand Module Table 6.1. Industry Categories. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 6.2.Retirement Rates. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries (Table 6.1). The manufacturing industries are modeled through the use of a detailed process flow or end use accounting

147

EIA - Assumptions to the Annual Energy Outlook 2009 - Electricity Market  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumptions to the Annual Energy Outlook 2009 Electricity Market Module figure 6. Electricity Market Model Supply Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand electricity, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2009, DOE/EIA-M068(2009). Based on fuel prices and electricity demands provided by the other modules

148

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2006 The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 12 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module forecasts energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region forecast using the SEDS27 data.

149

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7 7 1 (AEO2007), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant to formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports. 2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview 3 , which is updated once every few years. The National Energy Modeling System The projections in the AEO2007 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and

150

Assumptions to the Annual Energy Outlook 2001 - Household Expenditures  

Gasoline and Diesel Fuel Update (EIA)

Completed Copy in PDF Format Completed Copy in PDF Format Related Links Annual Energy Outlook2001 Supplemental Data to the AEO2001 NEMS Conference To Forecasting Home Page EIA Homepage Household Expenditures Module Key Assumptions The historical input data used to develop the HEM version for the AEO2001 consists of recent household survey responses, aggregated to the desired level of detail. Two surveys performed by the Energy Information Administration are included in the AEO2001 HEM database, and together these input data are used to develop a set of baseline household consumption profiles for the direct fuel expenditure analysis. These surveys are the 1997 Residential Energy Consumption Survey (RECS) and the 1991 Residential Transportation Energy Consumption Survey (RTECS). HEM uses the consumption forecast by NEMS for the residential and

151

EIA - Assumptions to the Annual Energy Outlook 2008 - Industrial Demand  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2008 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 21 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module projects energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region projection using the SEDS1 data.

152

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2006 Figure 7. Oil and Gas Supply Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas supply on a regional basis (Figure 7). A detailed description of the OGSM is provided in the EIA publication, Model Documentation Report: The Oil and Gas Supply Module (OGSM), DOE/EIA-M063(2006), (Washington, DC, 2006). The OGSM provides crude oil and natural gas short-term supply parameters to both the Natural Gas Transmission and Distribution Module and the Petroleum Market Module. The OGSM simulates the activity of numerous firms that produce oil and natural

153

Cost and Performance Assumptions for Modeling Electricity Generation Technologies  

Science Conference Proceedings (OSTI)

The goal of this project was to compare and contrast utility scale power plant characteristics used in data sets that support energy market models. Characteristics include both technology cost and technology performance projections to the year 2050. Cost parameters include installed capital costs and operation and maintenance (O&M) costs. Performance parameters include plant size, heat rate, capacity factor or availability factor, and plant lifetime. Conventional, renewable, and emerging electricity generating technologies were considered. Six data sets, each associated with a different model, were selected. Two of the data sets represent modeled results, not direct model inputs. These two data sets include cost and performance improvements that result from increased deployment as well as resulting capacity factors estimated from particular model runs; other data sets represent model input data. For the technologies contained in each data set, the levelized cost of energy (LCOE) was also evaluated, according to published cost, performance, and fuel assumptions.

Tidball, R.; Bluestein, J.; Rodriguez, N.; Knoke, S.

2010-11-01T23:59:59.000Z

154

Assumptions to the Annual Energy Outlook 2001 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for

155

Assumptions to the Annual Energy Outlook 2002 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for

156

Requirement for vasoactive amines for production of delayed-type hypersensitivity skin reactions  

E-Print Network (OSTI)

Injection of antigen into the dermis of the flank of an appropriately immunized rat, guinea pig, monkey, or man results, 24-48 h later, in the formation of an erythematous, indurated lesion. Similar skin testing of immunized mice generally fails to produce such lesions (1-3). The explanation for this particular difference between mice and men is unknown but there is reason to believe that it may not stem from differences in immunologically competent cells. Two observations support this view. (a) Appropriately immunized mice exhibit antigen-specific delayed-type hypersensitivity (DTH) ' reactions when the site of elicitation is the foot pad (4) or the ear (5). (b) Mice exhibit most other manifestations of cell-mediated immunity, in a normal fashion, despite their failure to produce DTH reactions in the flank skin. Thus, mice must have appropriately reactive T cells but there may be some difficulty in delivering the cells required for the production of DTH reactions to the flank skin. In support of this notion, it has been shown that ifperitoneal exudate cells are added to the eliciting dose of antigen placed in the flank skin the lesions that result are morphologically

K. Gershon; Philip W. Askenase; Michael; D. Gershon

1975-01-01T23:59:59.000Z

157

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

equilibrates supply and demand. Associated-dissolved gas production is determined in the Oil and Gas Supply Module (OGSM). Secondary flows are established before the equilibration...

158

Assumptions to the Annual Energy Outlook 2000-Table 1. Summary of the  

Gasoline and Diesel Fuel Update (EIA)

0 Cases 0 Cases Case Name Description Integration mode Reference Baseline economic growth, world oil price, and technology assumptions Fully Integrated Low Economic Growth Gross Domestic product grows at an average annual rate of 1.7 percent, compared to the reference case growth of 2.2 percent. Fully Integrated High Economic Growth Gross domestic product grows at an average annual rate of 2.6 percent, compared to the reference case growth of 2.2 percent. Fully Integrated Low World Oil Price World oil prices are $14.90 per barrel in 2020, compared to $22.04 per barrel in the reference case. Fully Integrated High World Oil Price World oil prices are $28.04 per barrel in 2020, compared to $22.04 per barrel in the reference case. Fully Integrated Residential: 2000 Technology

159

Assumptions to the Annual Energy Outlook 2001 - Table 1. Summary of AEO2001  

Gasoline and Diesel Fuel Update (EIA)

1 Cases 1 Cases Case name Description Integration mode Reference Baseline economic growth, world oil price, and technology assumptions Fully integrated Low Economic Growth Gross domestic product grows at an average annual rate of 2.5 percent, compared to the reference case growth of 3.0 percent. Fully integrated High Economic Growth Gross domestic product grows at an average annual rate of 3.5 percent, compared to the reference case growth of 3.0 percent. Fully integrated Low World Oil Price World oil prices are $15.10 per barrel in 2020, compared to $22.41 per barrel in the reference case. Fully integrated High World Oil Price World oil prices are $28.42 per barrel in 2020, compared to $22.41 per barrel in the reference case. Fully integrated Residential: 2001 Technology

160

EIA-Assumptions to the Annual Energy Outlook - Transportation Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2007 Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption isthe sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


161

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

unfinished oil imports, other refinery inputs (including alcohols, unfinished oil imports, other refinery inputs (including alcohols, ethers, and bioesters), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. The PMM contains a linear programming (LP) representation of U.S. refining activities in the five Petroleum Area Defense Districts (PADDs) (Figure 9). The LP model is created by aggregating individual refineries within a PADD into one representative refinery, and linking all five PADD's via crude and product transit links. This representation provides the marginal costs of production for a number of conventional and new petroleum products. In order to interact with other NEMS modules with different regional representations,

162

Assumptions to the Annual Energy Outlook 2001 - International...  

Gasoline and Diesel Fuel Update (EIA)

the supply prices of crude oils and petroleum products for import to the United States in response to changes in U.S. import requirements. A market clearing method is used to...

163

Assumptions to the Annual Energy Outlook 2002 - International...  

Gasoline and Diesel Fuel Update (EIA)

in the world oil price and the supply prices of crude oils and petroleum products for import to the United States in response to changes in U.S. import requirements. A market...

164

Development of gas production type curves for horizontal wells in coalbed methane reservoirs.  

E-Print Network (OSTI)

??Coalbed methane is an unconventional gas resource that consists of methane production from coal seams .The unique difference between CBM and conventional gas reservoirs is… (more)

Nfonsam, Allen Ekahnzok.

2006-01-01T23:59:59.000Z

165

A Statistical Analysis of the Dependency of Closure Assumptions in Cumulus Parameterization on the Horizontal Resolution  

Science Conference Proceedings (OSTI)

Simulated data from the UCLA cumulus ensemble model are used to investigate the quasi-universal validity of closure assumptions used in existing cumulus parameterizations. A closure assumption is quasi-universally valid if it is sensitive neither ...

Kuan-Man Xu

1994-12-01T23:59:59.000Z

166

Dialectical theory for multi-agent assumption-based planning  

Science Conference Proceedings (OSTI)

The purpose of this paper is to introduce a dialectical theory for plan synthesis based on a multi-agent approach. This approach is a promising way to devise systems based on agent planners in which the production of a global shared plan is obtained ...

Damien Pellier; Humbert Fiorino

2005-09-01T23:59:59.000Z

167

Assumptions to the Annual Energy Outlook 2000 - International Energy Module  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module determines changes in the world oil price and the supply prices of crude oils and petroleum products for import to the United States in response to changes in U.S. import requirements. A market clearing method is used to determine the price at which worldwide demand for oil is equal to the worldwide supply. The module determines new values for oil production and demand for regions outside the United States, along with a new world oil price that balances supply and demand in the international oil market. A detailed description of the International Energy Module is provided in the EIA publication, Model Documentation Report: The International Energy Module of the National Energy Modeling System, DOE/EIA-M071(99), (Washington, DC, February 1999).

168

EIA-Assumptions to the Annual Energy Outlook - National Gas Transmission  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumptions to the Annual Energy Outlook 2007 National Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and Distribution Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution

169

EIA - Assumptions to the Annual Energy Outlook 2009 - Oil and Gas Supply  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2009 Oil and Gas Supply Module Figure 7. Oil and Gas Supply Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. Table 9.1. Crude Oil Technically Recoverable Resources. Need help, contact the Naitonal Energy Information Center at 202-586-8800. printer-friendly version Table 9.2. Natural Gas Technically Recoverable Resources. Need help, contact the National Energy Information Center at 202-586-8800. Table 9.2. Continued printer-friendly version Table 9.3. Assumed Size and Initial Production year of Major Announced Deepwater Discoveries. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 9.4. Assumed Annual Rates of Technological Progress for Conventional Crude Oil and Natural Gas Sources. Need help, contact the National Energy Information Center at 202-586-8800.

170

Assumptions to the Annual Energy Outlook 2002 - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimates of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and

171

Assumptions to the Annual Energy Outlook 2001 - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimates of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and

172

Assumptions to the Annual Energy Outlook 1999 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

transportation.gif (5318 bytes) transportation.gif (5318 bytes) The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, industry sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs), freight trucks (>10,000 lbs), freight and passenger airplanes, freight rail, freight shipping, mass transit, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

173

Assumptions to the Annual Energy Outlook 2000 - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimates of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and Census Division and prices for each energy source for each of the nine Census Divisions. The Residential Demand Module also requires projections of available equipment over the forecast horizon. Over time, equipment efficiency tends to increase because of general technological advances and also because of Federal and/or state efficiency standards. As energy prices and available equipment changes over the forecast horizon, the module includes projected changes to the type and efficiency of equipment purchased as well as projected changes in the usage intensity of the equipment stock.

174

Assumptions to the Annual Energy Outlook 1999 - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

residential.gif (5487 bytes) residential.gif (5487 bytes) The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimates of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and Census Division and prices for each energy source for each of the nine Census Divisions. The Residential Demand Module also requires projections of available equipment over the forecast horizon. Over time, equipment efficiency tends to increase because of general technological advances and also because of Federal and/or state efficiency standards. As energy prices and available equipment changes over the forecast horizon, the module includes projected changes to the type and efficiency of equipment purchased as well as projected changes in the usage intensity of the equipment stock.

175

EIA-Assumptions to the Annual Energy Outlook - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2007 Residential Demand Module Figure 5. United States Census Divisions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the "unit energy consumption" by appliance (or UEC-in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new

176

EIA-Assumptions to the Annual Energy Outlook - Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2007 Commercial Demand Module The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2030. The definition of the commercial sector is consistent with EIA's State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.12

177

AEO2011: Coal Production by Region and Type This dataset comes...  

Open Energy Info (EERE)

by Region and Type This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is Table 140,...

178

Comparison of risk-dominant scenario assumptions for several TRU waste facilities in the DOE complex  

Science Conference Proceedings (OSTI)

In order to gain a risk management perspective, the DOE Rocky Flats Field Office (RFFO) initiated a survey of other DOE sites regarding risks from potential accidents associated with transuranic (TRU) storage and/or processing facilities. Recently-approved authorization basis documents at the Rocky Flats Environmental Technology Site (RFETS) have been based on the DOE Standard 3011 risk assessment methodology with three qualitative estimates of frequency of occurrence and quantitative estimates of radiological consequences to the collocated worker and the public binned into three severity levels. Risk Class 1 and 2 events after application of controls to prevent or mitigate the accident are designated as risk-dominant scenarios. Accident Evaluation Guidelines for selection of Technical Safety Requirements (TSRs) are based on the frequency and consequence bin assignments to identify controls that can be credited to reduce risk to Risk Class 3 or 4, or that are credited for Risk Class 1 and 2 scenarios that cannot be further reduced. This methodology resulted in several risk-dominant scenarios for either the collocated worker or the public that warranted consideration on whether additional controls should be implemented. RFFO requested the survey because of these high estimates of risks that are primarily due to design characteristics of RFETS TRU waste facilities (i.e., Butler-type buildings without a ventilation and filtration system, and a relatively short distance to the Site boundary). Accident analysis methodologies and key assumptions are being compared for the DOE sites responding to the survey. This includes type of accidents that are risk dominant (e.g., drum explosion, material handling breach, fires, natural phenomena, external events, etc.), source term evaluation (e.g., radionuclide material-at-risk, chemical and physical form, damage ratio, airborne release fraction, respirable fraction, leakpath factors), dispersion analysis (e.g., meteorological assumptions, distance to receptors, plume meander, deposition, and other factors affecting the calculated {chi}/Q), dose assessments (specific activities, inhalation dose conversion factors, breathing rates), designated frequency of occurrence, and risk assignment per the DOE Standard 3011 methodology. Information from the sites is being recorded on a spreadsheet to facilitate comparisons. The first response from Westinghouse Safety Management Solutions for the Savannah River Site (SRS) also provided a detailed analysis of the major differences in methods and assumptions between RFETS and SRS, which forms much of the basis for this paper. Other sites responding to the survey include the Idaho National Engineering and Environmental Laboratory (INEEL), Hanford, and the Los Alamos National Laboratory (LANL).

Foppe, T.L. [Foppe and Associates, Inc., Golden, CO (United States); Marx, D.R. [Westinghouse Safety Management Solutions, Inc., Aiken, SC (United States)

1999-06-01T23:59:59.000Z

179

Assumptions to the Annual Energy Outlook 2001 - Natural Gas Transmission  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution network that links them. In addition, natural gas flow patterns are a function of the pattern in the previous year, coupled with the relative prices of gas supply options as translated to the represented market

180

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

The International Energy Module (IEM) performs two tasks in all NEMS runs. First, the module reads The International Energy Module (IEM) performs two tasks in all NEMS runs. First, the module reads exogenously derived supply curves, initial price paths and international regional supply and demand levels into NEMS. These quantities are not modeled directly in NEMS because NEMS is not an international model. Previous versions of the IEM adjusted these quantities after reading in initial values. In an attempt to more closely integrate the AEO2007 with the IEO2006 and the STEO some functionality was removed from the IEM. More analyst time was devoted to analyzing price relationships between marker crude oils and refined products. A new exogenous oil supply model, Generate World Oil Balances (GWOB), was also developed to incorporate actual investment occurring in the international oil market through 2015

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


181

Assumptions to the Annual Energy Outlook 2001 - Macroeconomic Activity  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module

182

Assumptions to the Annual Energy Outlook 1999 - Macroeconomic Activity  

Gasoline and Diesel Fuel Update (EIA)

macroeconomic.gif (5367 bytes) macroeconomic.gif (5367 bytes) The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065, (Washington, DC, February 1994).

183

Assumptions to the Annual Energy Outlook 2002 - Macroeconomic Activity  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module

184

Assumptions to the Annual Energy Outlook 2000 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065, (Washington, DC, February 1994), plus Macroeconomic Activity Module (MAM): Kernel Regression Documentation of the National Energy Modeling System 1999, DOE/EIA-M065(99), Washington, DC, 1999).

185

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

12 12 . Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was one of the first electric generation technologies, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon the availability of low-cost

186

Assumptions to the Annual Energy Outlook 2002 - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has five submodules representing various renewable energy sources, biomass, geothermal, landfill gas, solar, and wind; a sixth renewable, conventional hydroelectric power, is represented in the Electricity Market Module (EMM).117 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration,

187

Assumptions to the Annual Energy Outlook 2002 - Natural Gas Transmission  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution network that links them. In addition, natural gas flow patterns are a function of the pattern in the previous year, coupled with the relative prices of gas supply options as translated to the represented market

188

Assumptions to the Annual Energy Outlook 2001 - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has five submodules representing various renewable energy sources, biomass, geothermal, landfill gas, solar, and wind; a sixth renewable, conventional hydroelectric power, is represented in the Electricity Market Module (EMM).112 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration,

189

Development of single type copper alumina catalysts for hydrogen production from dimethyl ether (DME)  

Science Conference Proceedings (OSTI)

Dimethyl ether (DME) is expected as one of clean fuels. We have been studying on DME steam reforming for hydrogen production. Copper alumina catalysts prepared by the sol-gel method produced large quantities of H2 with DME steam reforming. Aiming at ... Keywords: DME, alumina, catalyst, clean fuel, copper, dimethyl ether, hydrogen, sol-gel method, steam reforming

Kaoru Takeishi; Atsushi Ban

2010-02-01T23:59:59.000Z

190

Assumptions to the Annual Energy Outlook 2000 - Household Expenditures  

Gasoline and Diesel Fuel Update (EIA)

Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2020. The definition of the commercial sector is consistent with EIAÂ’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.12

191

Assumptions to the Annual Energy Outlook 1999 - Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

commercial.gif (5196 bytes) commercial.gif (5196 bytes) The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2020. The definition of the commercial sector is consistent with EIAÂ’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings, however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.12

192

Assumptions to the Annual Energy Outlook 2001 - Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2020. The definition of the commercial sector is consistent with EIAÂ’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for

193

Assumptions to the Annual Energy Outlook 2002 - Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2020. The definition of the commercial sector is consistent with EIAÂ’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for

194

U.S. Crude Oil and Petroleum Products Stocks by Type  

U.S. Energy Information Administration (EIA)

Stock Type: Area: Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 View History; Total Stocks: 1,790,732: 1,793,174: 1,806,501: 1,817,459: 1,817,679: 1,817,508: 1956-2013 ...

195

Affine Type A Crystal Structure on Tensor Products of Rectangles, Demazure Characters, and Nilpotent Varieties  

Science Conference Proceedings (OSTI)

Answering a question of Kuniba, Misra, Okado, Takagi, and Uchiyama, it is shown that certain higher level Demazure characters of affine type A, coincide with the graded characters of coordinate rings of closures of conjugacy classes of nilpotent matrices. Keywords: Kostka polynomial, Littlewood-Richardson coefficient, crystal graph, tableau

Mark Shimozono

2002-03-01T23:59:59.000Z

196

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind108. Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind108. Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was one of the first electric generation technologies, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon the availability of low-cost energy storage systems.

197

Assumptions to the Annual Energy Outlook 2000 - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module (RFM) consists of five distinct submodules that represent the major renewable energy technologies. Although it is described here, conventional hydroelectric is included in the Electricity Market Module (EMM) and is not part of the RFM. Similarly, ethanol modeling is included in the Petroleum Market Module (PMM). Some renewables, such as municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not require the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was an original source of electricity generation, to newer power systems using wind, solar, and geothermal energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon low-cost energy storage.

198

Assumptions to the Annual Energy Outlook 1999 - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

renewable.gif (4875 bytes) renewable.gif (4875 bytes) The NEMS Renewable Fuels Module (RFM) consists of five distinct submodules that represent the major renewable energy technologies. Although it is described here, conventional hydroelectric is included in the Electricity Market Module (EMM) and is not part of the RFM. Similarly, ethanol modeling is included in the Petroleum Market Module (PMM). Some renewables, such as municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not require the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was an original source of electricity generation, to newer power systems using wind, solar, and geothermal energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittence, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon low-cost energy storage.

199

Assumptions to the Annual Energy Outlook 1999 - Table 2  

Gasoline and Diesel Fuel Update (EIA)

Carbon Emission Factors (Kilograms-carbon per million Btu) Carbon Emission Factors (Kilograms-carbon per million Btu) Fuel Type Carbon Coefficient at Full Combustion Combustion Fraction Adjusted Emissions Factor Petroleum Motor Gasoline 19.35 0.990 19.16 Liquefied Petroleum Gas Used as Fuel 16.87 0.995 16.79 Used as Feedstock 17.11 0.200 3.42 Jet Fuel 19.33 0.990 19.14 Distillate Fuel 19.95 0.990 19.75 Residual Fuel 21.49 0.990 21.28 Asphalt and Road Oil 20.62 0.000 0.00 Lubricants 20.24 0.600 12.14 Petrochemical Feedstocks 19.37 0.200 3.87 Kerosene 19.72 0.990 19.52 Petroleum Coke 27.85 0.500 13.93 Petroleum Still Gas 17.51 0.995 17.42 Other Industrial 20.31 0.990 20.11 Coal Residential and Commercial 25.92 0.990 25.74 Metallurgical 25.55 0.990 25.28 Industrial Other 25.61 0.990 25.38 Electric Utility1 25.74 0.990 25.48 Natural Gas Used as Fuel

200

A Review of Electric Vehicle Cost Studies: Assumptions, Methodologies, and Results  

E-Print Network (OSTI)

assumptions Battery costs and capacities: Lead acid batteryElectricity cost Battery cost and capacity: Lead acidElectricity cost Battery cost and capacity: N i C d

Lipman, Timothy

1999-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


201

EIA-Assumptions to the Annual Energy Outlook - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module Assumptions to the Annual Energy Outlook 2007 Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind.112 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was one of the first electric generation technologies, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon the availability of low-cost energy storage systems.

202

Standard assumptions and methods for solar heating and cooling systems analysis  

DOE Green Energy (OSTI)

A set of inputs, assumptions, analytical methods, and a reporting format is presented to help compare the results of residential and commercial solar system analyses being performed by different investigators. By the common use of load data, meteorological data, economic parameters, and reporting format, researchers examining, for example, two types of collectors may more easily compare their results. For residential heating and cooling systems, three locations were selected. The weather data chosen to characterize these cities are the Typical Meteorological Year (TMY). A house for each location was defined that is typical of new construction in that locale. Hourly loads for each location were calculated using a computerized load model that interacts with the system specified inputs characterizing each house. Four locations for commercial cooling analyses were selected from among the existing sites for which TMYs were available. A light commercial (nominal 25-ton cooling load) office building was defined and is used in all four locations. Hourly cooling and heating loads were computed for each city and are available on magnetic tape from the Solar Energy Research Insititute (SERI).

Leboeuf, C.M.

1980-01-01T23:59:59.000Z

203

The purpose of this chapter is to illustrate the types of guns and other products that are available at gun shows. Details on the  

E-Print Network (OSTI)

4 What's for Sale The purpose of this chapter is to illustrate the types of guns and other products have specific types of firearms for sale at gun shows in California and in Arizona, Nevada, Texas that are available at gun shows. Details on the properties, use in crime, and lethality of particular firearms

Leistikow, Bruce N.

204

Heterogeneous Correlation Modeling Based on the Wavelet Diagonal Assumption and on the Diffusion Operator  

Science Conference Proceedings (OSTI)

This article discusses several models for background error correlation matrices using the wavelet diagonal assumption and the diffusion operator. The most general properties of filtering local correlation functions, with wavelet formulations, are ...

Olivier Pannekoucke

2009-09-01T23:59:59.000Z

205

Microwave Properties of Ice-Phase Hydrometeors for Radar and Radiometers: Sensitivity to Model Assumptions  

Science Conference Proceedings (OSTI)

A simplified framework is presented for assessing the qualitative sensitivities of computed microwave properties, satellite brightness temperatures, and radar reflectivities to assumptions concerning the physical properties of ice-phase ...

Benjamin T. Johnson; Grant W. Petty; Gail Skofronick-Jackson

2012-12-01T23:59:59.000Z

206

NGNP: High Temperature Gas-Cooled Reactor Key Definitions, Plant Capabilities, and Assumptions  

SciTech Connect

This document provides key definitions, plant capabilities, and inputs and assumptions related to the Next Generation Nuclear Plant to be used in ongoing efforts related to the licensing and deployment of a high temperature gas-cooled reactor. These definitions, capabilities, and assumptions were extracted from a number of NGNP Project sources such as licensing related white papers, previously issued requirement documents, and preapplication interactions with the Nuclear Regulatory Commission (NRC).

Wayne Moe

2013-05-01T23:59:59.000Z

207

Comparison of entropy production rates in two different types of self-organized flows: Benard convection and zonal flow  

Science Conference Proceedings (OSTI)

Two different types of self-organizing and sustaining ordered motion in fluids or plasmas--one is a Benard convection (or streamer) and the other is a zonal flow--have been compared by introducing a thermodynamic phenomenological model and evaluating the corresponding entropy production rates (EP). These two systems have different topologies in their equivalent circuits: the Benard convection is modeled by parallel connection of linear and nonlinear conductances, while the zonal flow is modeled by series connection. The ''power supply'' that drives the systems is also a determinant of operating modes. When the energy flux is a control parameter (as in usual plasma experiments), the driver is modeled by a constant-current power supply, and when the temperature difference between two separate boundaries is controlled (as in usual computational studies), the driver is modeled by a constant-voltage power supply. The parallel (series)-connection system tends to minimize (maximize) the total EP when a constant-current power supply drives the system. This minimum/maximum relation flips when a constant-voltage power supply is connected.

Kawazura, Y.; Yoshida, Z. [Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba 277-8561 (Japan)

2012-01-15T23:59:59.000Z

208

Annual Energy Outlook 2001-Appendix G: Major Assumptions for the Forecasts  

Gasoline and Diesel Fuel Update (EIA)

Forecasts Forecasts Summary of the AEO2001 Cases/ Scenarios - Appendix Table G1 bullet1.gif (843 bytes) Model Results (Formats - PDF, ZIP) - Appendix Tables - Reference Case - 1998 to 2020 bullet1.gif (843 bytes) Download Report - Entire AEO2001 (PDF) - AEO2001 by Chapters (PDF) bullet1.gif (843 bytes) Acronyms bullet1.gif (843 bytes) Contacts Related Links bullet1.gif (843 bytes) Assumptions to the AEO2001 bullet1.gif (843 bytes) Supplemental Data to the AEO2001 (Only available on the Web) - Regional and more detailed AEO 2001 Reference Case Results - 1998, 2000 to 2020 bullet1.gif (843 bytes) NEMS Conference bullet1.gif (843 bytes) Forecast Homepage bullet1.gif (843 bytes) EIA Homepage Appendix G Major Assumptions for the Forecasts Component Modules Major Assumptions for the Annual Energy Outlook 2001

209

On the use of the parabolic concentration profile assumption for a rotary desiccant dehumidifier  

SciTech Connect

The current work describes a model for a desiccant dehumidifier which uses a parabolic concentration profile assumption to model the diffusion resistance inside the desiccant particle. The relative merits of the parabolic concentration profile model compared with widely utilized rotary desiccant wheel models are discussed. The periodic steady-state parabolic concentration profile model developed is efficient and can accommodate a variety of materials. These features make it an excellent tool for design studies requiring repetitive desiccant wheel simulations. A quartic concentration profile assumption was also investigated which yielded a 2.8 percent average improvement in prediction error over the parabolic model.

Chant, E.E. [Univ. of Turabo, Gurabo (Puerto Rico); Jeter, S.M. [Georgia Inst. of Technology, Atlanta, GA (United States). George W. Woodruff School of Mechanical Engineering

1995-02-01T23:59:59.000Z

210

Sensitivity of Rooftop PV Projections in the SunShot Vision Study to Market Assumptions  

Science Conference Proceedings (OSTI)

The SunShot Vision Study explored the potential growth of solar markets if solar prices decreased by about 75% from 2010 to 2020. The SolarDS model was used to simulate rooftop PV demand for this study, based on several PV market assumptions--future electricity rates, customer access to financing, and others--in addition to the SunShot PV price projections. This paper finds that modeled PV demand is highly sensitive to several non-price market assumptions, particularly PV financing parameters.

Drury, E.; Denholm, P.; Margolis, R.

2013-01-01T23:59:59.000Z

211

Typed MSR: Syntax and Examples  

Science Conference Proceedings (OSTI)

Many design flaws and incorrect analyses of cryptographic protocols can be traced to inadequate specification languages for message components, environment assumptions, and goals. In this paper, we present MSR, a strongly typed specification language ...

Iliano Cervesato

2001-05-01T23:59:59.000Z

212

Biennial Assessment of the Fifth Power Plan Gas Turbine Power Plant Planning Assumptions  

E-Print Network (OSTI)

Biennial Assessment of the Fifth Power Plan Gas Turbine Power Plant Planning Assumptions October 17, 2006 Simple- and combined-cycle gas turbine power plants fuelled by natural gas are among the bulk-emission and efficient gas turbine technology made combined-cycle gas turbine power plants the "resource of choice

213

External review of the thermal energy storage (TES) cogeneration study assumptions. Final report  

DOE Green Energy (OSTI)

This report is to provide a detailed review of the basic assumptions made in the design, sizing, performance, and economic models used in the thermal energy storage (TES)/cogeneration feasibility studies conducted by Pacific Northwest Laboratory (PNL) staff. This report is the deliverable required under the contract.

Lai, B.Y.; Poirier, R.N. [Chicago Bridge and Iron Technical Services Co., Plainfield, IL (United States)

1996-08-01T23:59:59.000Z

214

A New Type Curve Analysis for Shale Gas/Oil Reservoir Production Performance with Dual Porosity Linear System.  

E-Print Network (OSTI)

??With increase of interest in exploiting shale gas/oil reservoirs with multiple stage fractured horizontal wells, complexity of production analysis and reservoir description have also increased.… (more)

Abdulal, Haider Jaffar

2012-01-01T23:59:59.000Z

215

Production  

E-Print Network (OSTI)

There are serious concerns about the greenhouse gas (GHG) emissions, energy and nutrient and water use efficiency of large-scale, first generation bio-energy feedstocks currently in use. A major question is whether biofuels obtained from these feedstocks are effective in combating climate change and what impact they will have on soil and water resources. Another fundamental issue relates to the magnitude and nature of their impact on food prices and ultimately on the livelihoods of the poor. A possible solution to overcome the current potentially large negative effects of large-scale biofuel production is developing second and third generation conversion techniques from agricultural residues and wastes and step up the scientific research efforts to achieve sustainable biofuel production practices. Until such sustainable techniques are available governments should scale back their support for and promotion of biofuels. Multipurpose feedstocks should be investigated making use of the bio-refinery concept (bio-based economy). At the same time, the further development of non-commercial, small scale

Science Council Secretariat

2008-01-01T23:59:59.000Z

216

Paducah DUF6 Conversion Final EIS - Chapter 4: Environmental Impact Assessment Approach, Assumptions, and Methodology  

NLE Websites -- All DOE Office Websites (Extended Search)

Paducah DUF Paducah DUF 6 Conversion Final EIS 4 ENVIRONMENTAL IMPACT ASSESSMENT APPROACH, ASSUMPTIONS, AND METHODOLOGY This EIS evaluates potential impacts on human health and the natural environment from building and operating a DUF 6 conversion facility at three alternative locations at the Paducah site and for a no action alternative. These impacts might be positive, in that they would improve conditions in the human or natural environment, or negative, in that they would cause a decline in those conditions. This chapter provides an overview of the methods used to estimate the potential impacts associated with the EIS alternatives, summarizes the major assumptions that formed the basis of the evaluation, and provides some background information on human health

217

NGNP: High Temperature Gas-Cooled Reactor Key Definitions, Plant Capabilities, and Assumptions  

SciTech Connect

This document is intended to provide a Next Generation Nuclear Plant (NGNP) Project tool in which to collect and identify key definitions, plant capabilities, and inputs and assumptions to be used in ongoing efforts related to the licensing and deployment of a high temperature gas-cooled reactor (HTGR). These definitions, capabilities, and assumptions are extracted from a number of sources, including NGNP Project documents such as licensing related white papers [References 1-11] and previously issued requirement documents [References 13-15]. Also included is information agreed upon by the NGNP Regulatory Affairs group's Licensing Working Group and Configuration Council. The NGNP Project approach to licensing an HTGR plant via a combined license (COL) is defined within the referenced white papers and reference [12], and is not duplicated here.

Phillip Mills

2012-02-01T23:59:59.000Z

218

GRI baseline projection: Methodology and assumptions 1996 edition. Topical report, January-December 1995  

Science Conference Proceedings (OSTI)

The report documents the methodology employed in producing the 1996 Edition of the GRI Baseline Projection. DRI/McGraw-Hill`s Energy Group (DRI) maintains an energy modeling system for the Gas Research Institute (GRI) that is used to produce an annual projection of the supply and demand for energy by regions in the United States. The 1996 Edition of the GRI Baseline Projection is produced using several different models. The models analyze various pieces of the U.S. energy markets and their solutions are based on a framework of exogenous assumptions provided by GRI. The report describes the integration and solution procedures of the models and the assumptions used to produce the final projection results.

Rhodes, M.R.; Baxter, R.P.; Nottingham, R.P.

1996-04-01T23:59:59.000Z

219

GRI baseline projection: Methodology and assumptions 1995 edition. Topical report, January-December 1994  

SciTech Connect

The report documents the methodology employed in producing the 1995 Edition of the GRI Baseline Projection. DRI/McGraw-Hill`s Energy Group (DRI) maintains an energy modeling system for the Gas Research Institute (GRI) that is used to produce an annual projection of the supply and demand for energy by regions in the United States. The 1995 Edition of the GRI Baseline Projection is produced using several different models. The models analyze various pieces of the U.S. energy markets and their solutions are based on a framework of exogeneous assumptions provided by GRI. The report describes the integration and solution procedures of the models and the assumptions used to produce the final projection results.

Baxter, R.P.; Silveira, T.S.; Harshbarger, S.L.

1995-02-01T23:59:59.000Z

220

EIA-Assumptions to the Annual Energy Outlook - Electricity Market Module  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumptions to the Annual Energy Outlook 2007 Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules-electricity capacity planning, electricity fuel dispatching, load and demand electricity, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2007, DOE/EIA- M068(2007). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


221

Gluconeogenesis as a system : development of in vivo flux analysis of hepatic glucose production in Type 2 Diabetes  

E-Print Network (OSTI)

Metabolic diseases are an increasing health concern in the developed world. Type 2 Diabetes, (T2D) affects over 100 million people worldwide and significantly contributes to chronic diseases such as atherosclerosis and ...

Alemán, José O. (José Orlando)

2008-01-01T23:59:59.000Z

222

Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results  

E-Print Network (OSTI)

A comparison of national energy consumption by fuel typeenergy consumption in homes under differing assumptions, scenarios, and policies. At the national

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

223

Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results  

E-Print Network (OSTI)

G. Koomey. 1994. Residential Appliance Data, Assumptions andunits) Table A 3 : Number of Appliances in Existing Homes (sector, including appliances and heating, ventilation, and

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

224

Energy consumption for baking and characteristics of baked product in relation to type of oven and baking procedure  

Science Conference Proceedings (OSTI)

Data were obtained and analyzed on 1) energy consumption when a test food was baked with four types of electric ovens (electric range oven, table model conventional oven, table model forced convection oven, and table model broiler/toaster oven) with three cooking procedures (participant's own procedure, preheated procedure, and cold start procedure); 2) patterns of energy consumption in 10-minute intervals; and 3) the characteristics of the finished food. Twenty participants from households in Columbus, Ohio, baked loaves of quick bread in a laboratory in the four types of ovens with three cooking procedures. Statistical analyses of data included analyses of variance, Tukey test, and Duncan's multiple range test. Significantly more energy was used with the participants' own procedures than with either the preheated or the cold start procedure (p < .01). There was no consistency in total energy consumption between the preheated and the cold start procedures in the four types of ovens. The electric range oven consumed significantly more energy than the other three types of ovens in the first 10-minute interval; however, the table model forced convection oven consumed significantly more energy than other ovens in the second and third 10-minute intervals. No consistent patterns were observed for volume and weight loss of breads baked with the three cooking procedures, but use of the table model forced convection oven always resulted in larger volume than with other ovens.

Nee, Y.

1982-01-01T23:59:59.000Z

225

Science with the Square Kilometer Array: Motivation, Key Science Projects, Standards and Assumptions  

E-Print Network (OSTI)

The Square Kilometer Array (SKA) represents the next major, and natural, step in radio astronomical facilities, providing two orders of magnitude increase in collecting area over existing telescopes. In a series of meetings, starting in Groningen, the Netherlands (August 2002) and culminating in a `science retreat' in Leiden (November 2003), the SKA International Science Advisory Committee (ISAC), conceived of, and carried-out, a complete revision of the SKA science case (to appear in New Astronomy Reviews). This preface includes: (i) general introductory material, (ii) summaries of the key science programs, and (iii) a detailed listing of standards and assumptions used in the revised science case.

C. Carilli; S. Rawlings

2004-09-12T23:59:59.000Z

226

Residential Appliance Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1  

E-Print Network (OSTI)

LBL-34046 UC-350 Residential Appliance Data, Assumptions and Methodology for End-Use Forecasting. DE-AC03-76SF00098 #12;i ABSTRACT This report details the data, assumptions and methodology for end-use provided by the Appliance Model in the Residential End-Use Energy Planning System (REEPS), which

227

Use of an ions thruster to dispose of type II long-lived fission products into outer space  

DOE Green Energy (OSTI)

To dispose of long-lived fission products (LLFPs) into outer space, an ions thruster can be used instead of a static accelerator. The specifications of the ions thrusters which are presently studies for space propulsion are presented, and their usability discussed. Using of a rocket with an ions thruster for disposing of the LLFPs directly into the sun required a larger amount of energy than does the use of an accelerator.

Takahashi, H.; Yu, A.

1997-04-01T23:59:59.000Z

228

Isospin and symmetry energy effects on nuclear fragment production in liquid-gas type phase transition region  

E-Print Network (OSTI)

We have demonstrated that the isospin of nuclei influences the fragment production during the nuclear liquid-gas phase transition. Calculations for Au197, Sn124, La124 and Kr78 at various excitation energies were carried out on the basis of the statistical multifragmentation model (SMM). We analyzed the behavior of the critical exponent tau with the excitation energy and its dependence on the critical temperature. Relative yields of fragments were classified with respect to the mass number of the fragments in the transition region. In this way, we have demonstrated that nuclear multifragmentation exhibits a 'bimodality' behavior. We have also shown that the symmetry energy has a small influence on fragment mass distribution, however, its effect is more pronounced in the isotope distributions of produced fragments.

N. Buyukcizmeci; R. Ogul; A. S. Botvina

2005-06-06T23:59:59.000Z

229

Isospin and symmetry energy effects on nuclear fragment production in liquid-gas type phase transition region  

E-Print Network (OSTI)

We have demonstrated that the isospin of nuclei influences the fragment production during the nuclear liquid-gas phase transition. Calculations for Au197, Sn124, La124 and Kr78 at various excitation energies were carried out on the basis of the statistical multifragmentation model (SMM). We analyzed the behavior of the critical exponent tau with the excitation energy and its dependence on the critical temperature. Relative yields of fragments were classified with respect to the mass number of the fragments in the transition region. In this way, we have demonstrated that nuclear multifragmentation exhibits a 'bimodality' behavior. We have also shown that the symmetry energy has a small influence on fragment mass distribution, however, its effect is more pronounced in the isotope distributions of produced fragments.

N. Buyukcizmeci; R. Ogul; A. S. Botvina

2004-12-31T23:59:59.000Z

230

EIA-Assumptions to the Annual Energy Outlook - Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2007 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 21 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module forecasts energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region forecast using the SEDS25 data.

231

EIA-Assumptions to the Annual Energy Outlook - Oil and Gas Supply Module  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2007 Oil and Gas Supply Module Figure 7. Oil and Gas Supply Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas supply on a regional basis (Figure 7). A detailed description of the OGSM is provided in the EIA publication, Model Documentation Report: The Oil and Gas Supply Module (OGSM), DOE/EIA-M063(2006), (Washington, DC, 2006). The OGSM provides crude oil and natural gas short-term supply parameters to both the Natural Gas Transmission and Distribution Module and the Petroleum Market Module. The OGSM simulates the activity of numerous firms that produce oil and natural

232

EIA - Assumptions to the Annual Energy Outlook 2008 - Oil and Gas Supply  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2008 Oil and Gas Supply Module Figure 7. Oil and Gas Supply Module. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas supply on a regional basis (Figure 7). A detailed description of the OGSM is provided in the EIA publication, Model Documentation Report: The Oil and Gas Supply Module (OGSM), DOE/EIA-M063(2007), (Washington, DC, 2007). The OGSM provides crude oil and natural gas short-term supply parameters to both the Natural Gas Transmission and Distribution Module and the Petroleum Market Module. The OGSM simulates the activity of numerous firms that produce oil and natural

233

Assumptions to the Annual Energy Outlook - Oil and Gas Supply Module  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumption to the Annual Energy Outlook Oil and Gas Supply Module Figure 7. Oil and Gas Supply Model Regions. Having problems, call our National Energy Information Center at 202-586-8800 for help. Table 50. Crude Oil Technically Recoverable Resources (Billion barrels) Printer Friendly Version Crude Oil Resource Category As of January 1, 2002 Undiscovered 56.02 Onshore 19.33 Northeast 1.47 Gulf Coast 4.76 Midcontinent 1.12 Southwest 3.25 Rocky Moutain 5.73 West Coast 3.00 Offshore 36.69 Deep (>200 meter W.D.) 35.01 Shallow (0-200 meter W.D.) 1.69 Inferred Reserves 49.14 Onshore 37.78 Northeast 0.79 Gulf Coast 0.80 Midcontinent 3.73 Southwest 14.61 Rocky Mountain 9.91 West Coast 7.94

234

EIA - Assumptions to the Annual Energy Outlook 2010 - Oil and Gas Supply  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2010 Oil and Gas Supply Module Figure 8. Natural Gas Transmission and Distribution Model Regions. The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas natural gas exploration and development on a regional basis (Figure 7). The OGSM is organized into 4 submodules: Onshore Lower 48 Oil and Gas Supply Submodule, Offshore Oil and Gas Supply Submodule, Oil Shale Supply submodule, and Alaska Oil and Gas Supply Submodule. A detailed description of the OGSM is provided in the EIA publication, Model Documentation Report: The Oil and Gas Supply Module (OGSM), DOE/EIA-M063(2010), (Washington, DC, 2010). The OGSM provides crude oil and natural gas short-term supply parameters to both the Natural

235

Washington International Renewable Energy Conference 2008 Pledges: Methodology and Assumptions Summary  

Science Conference Proceedings (OSTI)

The 2008 Washington International Renewable Energy Conference (WIREC) was held in Washington, D.C., from March 4-6, 2008, and involved nearly 9,000 people from 125 countries. The event brought together worldwide leaders in renewable energy (RE) from governments, international organizations, nongovernmental organizations, and the private sector to discuss the role that renewables can play in alleviating poverty, growing economies, and passing on a healthy planet to future generations. The conference concluded with more than 140 governments, international organizations, and private-sector representatives pledging to advance the uptake of renewable energy. The U.S. government authorized the National Renewable Energy Laboratory (NREL) to estimate the carbon dioxide (CO2) savings that would result from the pledges made at the 2008 conference. This report describes the methodology and assumptions used by NREL in quantifying the potential CO2 reductions derived from those pledges.

Babiuch, B.; Bilello, D. E.; Cowlin, S. C.; Mann, M.; Wise, A.

2008-08-01T23:59:59.000Z

236

Design assumptions and bases for small D-T-fueled spherical tokamak (ST) fusion core  

SciTech Connect

Recent progress in defining the assumptions and clarifying the bases for a small D-T-fueled ST fusion core are presented. The paper covers several issues in the physics of ST plasmas, the technology of neutral beam injection, the engineering design configuration, and the center leg material under intense neutron irradiation. This progress was driven by the exciting data from pioneering ST experiments, a heightened interest in proof-of-principle experiments at the MA level in plasma current, and the initiation of the first conceptual design study of the small ST fusion core. The needs recently identified for a restructured fusion energy sciences program have provided a timely impetus for examining the subject of this paper. Our results, though preliminary in nature, strengthen the case for the potential realism and attractiveness of the ST approach.

Peng, Yueng Kay Martin [ORNL; Haines, J.R. [Oak Ridge National Laboratory (ORNL)

1996-01-01T23:59:59.000Z

237

CRITICAL ASSUMPTIONS IN THE F-TANK FARM CLOSURE OPERATIONAL DOCUMENTATION REGARDING WASTE TANK INTERNAL CONFIGURATIONS  

SciTech Connect

The intent of this document is to provide clarification of critical assumptions regarding the internal configurations of liquid waste tanks at operational closure, with respect to F-Tank Farm (FTF) closure documentation. For the purposes of this document, FTF closure documentation includes: (1) Performance Assessment for the F-Tank Farm at the Savannah River Site (hereafter referred to as the FTF PA) (SRS-REG-2007-00002), (2) Basis for Section 3116 Determination for Closure of F-Tank Farm at the Savannah River Site (DOE/SRS-WD-2012-001), (3) Tier 1 Closure Plan for the F-Area Waste Tank Systems at the Savannah River Site (SRR-CWDA-2010-00147), (4) F-Tank Farm Tanks 18 and 19 DOE Manual 435.1-1 Tier 2 Closure Plan Savannah River Site (SRR-CWDA-2011-00015), (5) Industrial Wastewater Closure Module for the Liquid Waste Tanks 18 and 19 (SRRCWDA-2010-00003), and (6) Tank 18/Tank 19 Special Analysis for the Performance Assessment for the F-Tank Farm at the Savannah River Site (hereafter referred to as the Tank 18/Tank 19 Special Analysis) (SRR-CWDA-2010-00124). Note that the first three FTF closure documents listed apply to the entire FTF, whereas the last three FTF closure documents listed are specific to Tanks 18 and 19. These two waste tanks are expected to be the first two tanks to be grouted and operationally closed under the current suite of FTF closure documents and many of the assumptions and approaches that apply to these two tanks are also applicable to the other FTF waste tanks and operational closure processes.

Hommel, S.; Fountain, D.

2012-03-28T23:59:59.000Z

238

Types of Costs Types of Cost Estimates  

E-Print Network (OSTI)

· Types of Costs · Types of Cost Estimates · Methods to estimate capital costs MIN E 408: Mining the equipment for reclamation? Types of Costs #12;· Marginal Cost: ­ Change in total cost ­ Any production process involves fixed and variable costs. As production increases/expands, fixed costs are unchanged, so

Boisvert, Jeff

239

User cost in oil production  

E-Print Network (OSTI)

The assumption of an initial fixed mineral stock is superfluous and wrong. User cost (resource rent) in mineral production is the present value of expected increases in development cost. It can be measured as the difference ...

Adelman, Morris Albert

1990-01-01T23:59:59.000Z

240

Assumptions to the Annual Energy Outlook 2000 - Oil and Gas Supply...  

Annual Energy Outlook 2012 (EIA)

Resources Table 46. Natural Gas Technically Recoverable Resources Alaskan Natural Gas The outlook for natural gas production from the North Slope of Alaska is affected...

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241

50-year-old assumptions about strength muscled aside | Argonne National  

NLE Websites -- All DOE Office Websites (Extended Search)

C. David Williams with an X-ray diffraction apparatus used to measure lattice spacing of filaments in moth wing muscle samples. To view a larger version of the image, click on it. Credit: A. Kidder/University of Washington. C. David Williams with an X-ray diffraction apparatus used to measure lattice spacing of filaments in moth wing muscle samples. To view a larger version of the image, click on it. Credit: A. Kidder/University of Washington. C. David Williams with an X-ray diffraction apparatus used to measure lattice spacing of filaments in moth wing muscle samples. To view a larger version of the image, click on it. Credit: A. Kidder/University of Washington. To view a larger, downloadable version of the image, click on it. To view a larger, downloadable version of the image, click on it. 50-year-old assumptions about strength muscled aside July 11, 2013 Tweet EmailPrint LEMONT, Ill. - Doctors have a new way of thinking about how to treat heart and skeletal muscle diseases. Body builders have a new way of

242

The Role Of Modeling Assumptions And Policy Instruments in Evaluating The Global Implications Of U.S. Biofuel Policies  

Science Conference Proceedings (OSTI)

The primary objective of current U.S. biofuel law the Energy Independence and Security Act of 2007 (EISA) is to reduce dependence on imported oil, but the law also requires biofuels to meet carbon emission reduction thresholds relative to petroleum fuels. EISA created a renewable fuel standard with annual targets for U.S. biofuel use that climb gradually from 9 billion gallons per year in 2008 to 36 billion gallons (or about 136 billion liters) of biofuels per year by 2022. The most controversial aspects of the biofuel policy have centered on the global social and environmental implications of its potential land use effects. In particular, there is an ongoing debate about whether indirect land use change (ILUC) make biofuels a net source, rather sink, of carbon emissions. However, estimates of ILUC induced by biofuel production and use can only be inferred through modeling. This paper evaluates how model structure, underlying assumptions, and the representation of policy instruments influence the results of U.S. biofuel policy simulations. The analysis shows that differences in these factors can lead to divergent model estimates of land use and economic effects. Estimates of the net conversion of forests and grasslands induced by U.S. biofuel policy range from 0.09 ha/1000 gallons described in this paper to 0.73 ha/1000 gallons from early studies in the ILUC change debate. We note that several important factors governing LUC change remain to be examined. Challenges that must be addressed to improve global land use change modeling are highlighted.

Oladosu, Gbadebo A [ORNL; Kline, Keith L [ORNL

2010-01-01T23:59:59.000Z

243

Assumptions to the Annual Energy Outlook 2001 - Table 4. Coefficients of  

Gasoline and Diesel Fuel Update (EIA)

Coefficients of Linear Equations for Natural Gas- and Coefficients of Linear Equations for Natural Gas- and Oil-Related Methane Emissions Emissions Sources Intercept Variable Name and Units Coefficient Variable Name and Units Coefficient Natural Gas -38.77 Time trend (calendar year) .02003 Dry gas production (thousand cubic feet .02186 Natural Gas Processing -0.9454 Natural gas liquids production (million barrels per day) .9350 Not applicable Natural Gas Transmission and Storage 2.503 Pipeline fuel use (thousand cubic feet) 1.249 Dry gas production (thousand cubic feet) -0.06614 Natural Gas Distribution -58.16 Time trend (calendar year) .0297 Natural gas consumption (quadrillion Btu) .0196 Oil production, Refining, and Transport 0.03190 Oil consumption (quadrillion Btu) .002764 Not applicable Source: Derived from data used in Energy Information Administration, Emissions of Greenhouse Gases in the United States 1999, DOE/EIA-0573(99), (Washington, DC, October 2000).

244

Types of Costs Types of Cost Estimates  

E-Print Network (OSTI)

05-1 · Types of Costs · Types of Cost Estimates · Methods to estimate capital costs MIN E 408) costs apply to those items that are consumed in production process and are roughly proportional to level in cash flow analysis and in the decision to use the equipment for reclamation? Types of Costs #12

Boisvert, Jeff

245

Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1  

E-Print Network (OSTI)

LBL-34045 UC-1600 Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting-uses include Heating, Ventilation and Air Conditioning (HVAC). Our analysis uses the modeling framework provided by the HVAC module in the Residential End-Use Energy Planning System (REEPS), which was developed

246

Preliminary Review of Models, Assumptions, and Key Data used in Performance Assessments and Composite Analysis at the Idaho National Laboratory  

SciTech Connect

This document is in response to a request by Ming Zhu, DOE-EM to provide a preliminary review of existing models and data used in completed or soon to be completed Performance Assessments and Composite Analyses (PA/CA) documents, to identify codes, methodologies, main assumptions, and key data sets used.

Arthur S. Rood; Swen O. Magnuson

2009-07-01T23:59:59.000Z

247

A Graphical Approach to Diagnosing the Validity of the Conditional Independence Assumptions of a Bayesian Network Given Data  

SciTech Connect

Bayesian networks have attained widespread use in data analysis and decision making. Well studied topics include: efficient inference, evidence propagation, parameter learning from data for complete and incomplete data scenarios, expert elicitation for calibrating Bayesian network probabilities, and structure learning. It is not uncommon for the researcher to assume the structure of the Bayesian network or to glean the structure from expert elicitation or domain knowledge. In this scenario, the model may be calibrated through learning the parameters from relevant data. There is a lack of work on model diagnostics for fitted Bayesian networks; this is the contribution of this paper. We key on the definition of (conditional) independence to develop a graphical diagnostic method which indicates if the conditional independence assumptions imposed when one assumes the structure of the Bayesian network are supported by the data. We develop the approach theoretically and describe a Monte Carlo method to generate uncertainty measures for the consistency of the data with conditional independence assumptions under the model structure. We describe how this theoretical information and the data are presented in a graphical diagnostic tool. We demonstrate the approach through data simulated from Bayesian networks under different conditional independence assumptions. We also apply the diagnostic to a real world data set. The results indicate that our approach is a reasonable way of visualizing and inspecting the conditional independence assumption of a Bayesian network given data.

Walsh, Stephen J.; Whitney, Paul D.

2012-12-14T23:59:59.000Z

248

TYPE OF UPERATICIN  

Office of Legacy Management (LM)

Process i Theoretical Studies Sample & Analysis 0 Production 0 DisposalStorage a Facility Type 0 Manufacturing q University, a Research Organizatiori 0 Government Sponsored...

249

MODELING ASSUMPTIONS FOR THE ADVANCED TEST REACTOR FRESH FUEL SHIPPING CONTAINER  

SciTech Connect

The Advanced Test Reactor Fresh Fuel Shipping Container (ATR FFSC) is currently licensed per 10 CFR 71 to transport a fresh fuel element for either the Advanced Test Reactor, the University of Missouri Research Reactor (MURR), or the Massachusetts Institute of Technology Research Reactor (MITR-II). During the licensing process, the Nuclear Regulatory Commission (NRC) raised a number of issues relating to the criticality analysis, namely (1) lack of a tolerance study on the fuel and packaging, (2) moderation conditions during normal conditions of transport (NCT), (3) treatment of minor hydrogenous packaging materials, and (4) treatment of potential fuel damage under hypothetical accident conditions (HAC). These concerns were adequately addressed by modifying the criticality analysis. A tolerance study was added for both the packaging and fuel elements, full-moderation was included in the NCT models, minor hydrogenous packaging materials were included, and fuel element damage was considered for the MURR and MITR-II fuel types.

Rick J. Migliore

2009-09-01T23:59:59.000Z

250

Assumptions to the Annual Energy Outlook 2000-Table 2. Carbon Emission  

Gasoline and Diesel Fuel Update (EIA)

Carbon Emission Factors Carbon Emission Factors (Kilograms-carbon per million Btu) Fuel Type Carbon Coefficient at Full Combustion Combustion Fraction Adjusted Emissions Factor Petroleum Motor Gasoline 19.33 0.990 19.14 Liquefied Petroleum Gas Used as Fuel 17.20 0.995 17.11 Used as Feedstock 16.87 0.200 3.37 Jet Fuel 19.33 0.990 19.14 Distillate Fuel 19.95 0.990 19.75 Residual Fuel 21.49 0.990 21.28 Asphalt and Road Oil 20.62 0.000 0.00 Lubricants 20.24 0.600 12.14 Petrochemical Feedstocks 19.37 0.200 3.87 Kerosene 19.72 0.990 19.52 Petroleum Coke 27.85 0.500 13.93 Petroleum Still Gas 17.51 0.995 17.42 Other Industrial 20.31 0.990 20.11 Coal Residential and Commercial 25.92 0.990 25.66 Metallurgical 25.55 0.990 25.29 Industrial Other 25.61 0.990 25.39 Electric Utility1 25.74 0.990 24.486 Natural Gas Used as Fuel

251

Assumptions to the Annual Energy Outlook 2001 - Table 2. Carbon Dioxide  

Gasoline and Diesel Fuel Update (EIA)

Carbon Dioxide Emission Factors Carbon Dioxide Emission Factors (Kilograms-carbon equivalent per million Btu) Fuel Type Carbon Dioxide Coefficient at Full Combustion Combustion Fraction Adjusted Emissions Factor Petroleum Motor Gasoline 19.36 0.990 19.17 Liquefied Petroleum Gas Used as Fuel 17.18 0.995 17.09 Used as Feedstock 16.88 0.200 3.38 Jet Fuel 19.33 0.990 19.14 Distillate Fuel 19.95 0.990 19.75 Residual Fuel 21.49 0.990 21.28 Asphalt and Road Oil 20.62 0.000 0.00 Lubricants 20.24 0.600 12.14 Petrochemical Feedstocks 19.37 0.200 3.87 Kerosene 19.72 0.990 19.52 Petroleum Coke 27.85 0.500 13.93 Petroleum Still Gas 17.51 0.995 17.42 Other Industrial 20.31 0.990 20.11 Coal Residential and Commercial 26.00 0.990 25.74 Metallurgical 25.56 0.990 25.30 Industrial Other 25.63 0.990 25.38 Electric Utility1 25.76 0.990 25.50 Natural Gas

252

Facility Type!  

Office of Legacy Management (LM)

ITY: ITY: --&L~ ----------- srct-r~ -----------~------~------- if yee, date contacted ------------- cl Facility Type! i I 0 Theoretical Studies Cl Sample 84 Analysis ] Production 1 Diepasal/Storage 'YPE OF CONTRACT .--------------- 1 Prime J Subcontract&- 1 Purchase Order rl i '1 ! Other information (i.e., ---------~---~--~-------- :ontrait/Pirchaee Order # , I C -qXlJ- --~-------~~-------~~~~~~ I I ~~~---~~~~~~~T~~~ FONTRACTING PERIODi IWNERSHIP: ,I 1 AECIMED AECMED GOVT GOUT &NTtiAC+OR GUN-I OWNED ----- LEEE!? M!s LE!Ps2 -LdJG?- ---L .ANDS ILJILDINGS X2UIPilENT IRE OR RAW HA-I-L :INAL PRODUCT IASTE Z. RESIDUE I I kility l pt I ,-- 7- ,+- &!d,, ' IN&"E~:EW AT SITE -' ---------------- , . Control 0 AEC/tlED managed operations

253

CCN predictions using simplified assumptions of organic aerosol composition and mixing state: A synthesis from six different locations  

SciTech Connect

An accurate but simple quantification of the fraction of aerosol particles that can act as cloud condensation nuclei (CCN) is needed for implementation in large-scale models. Data on aerosol size distribution, chemical composition, and CCN concentration from six different locations have been analyzed to explore the extent to which simple assumptions of composition and mixing state of the organic fraction can reproduce measured CCN number concentrations. Fresher pollution aerosol as encountered in Riverside, CA, and the ship channel in Houston, TX, cannot be represented without knowledge of more complex (size-resolved) composition. For aerosol that has experienced processing (Mexico City, Holme Moss (UK), Point Reyes (CA), and Chebogue Point (Canada)), CCN can be predicted within a factor of two assuming either externally or internally mixed soluble organics although these simplified compositions/mixing states might not represent the actual properties of ambient aerosol populations, in agreement with many previous CCN studies in the literature. Under typical conditions, a factor of two uncertainty in CCN concentration due to composition assumptions translates to an uncertainty of {approx}15% in cloud drop concentration, which might be adequate for large-scale models given the much larger uncertainty in cloudiness.

Ervens, B.; Wang, J.; Cubison, M. J.; Andrews, E.; Feingold, G.; Ogren, J. A.; Jimenez, J. L.; Quinn, P. K.; Bates, T. S.; Zhang, Q.; Coe, H.; Flynn, M.; Allan, J. D.

2010-05-01T23:59:59.000Z

254

Statistical Approaches and Assumptions  

Science Conference Proceedings (OSTI)

... during the PCR amplification process – This is highly affected by DNA quantity and quality ... PCR inhibitors present in the sample may reduce PCR ...

2012-10-16T23:59:59.000Z

255

Assessment of Gasification-Based Biorefining at Kraft Pulp and Paper Mills in the United States, Part A: Background and Assumptions  

Science Conference Proceedings (OSTI)

Commercialization of black liquor and biomass gasification technologies is anticipated in the 2010-2015 time frame, and synthesis gas from gasifiers can be converted into liquid fuels using catalytic synthesis technologies that are already commercially established in the gas-to-liquids or coal-to-liquids industries. This set of two papers describes key results from a major assessment of the prospective energy, environmental, and financial performance of commercial gasification-based biorefineries integrated with kraft pulp and paper mills [1]. Seven detailed biorefinery designs were developed for a reference mill in the southeastern United States, together with the associated mass/energy balances, air emissions estimates, and capital investment requirements. The biorefineries provide chemical recovery services and co-produce process steam for the mill, some electricity, and one of three liquid fuels: a Fischer-Tropsch synthetic crude oil (which could be refined to vehicle fuels at an existing petroleum refinery), dimethyl ether (a diesel engine fuel or propane substitute), or an ethanol-rich mixed-alcohol product. This paper describes the key assumptions that underlie the biorefinery designs. Part B will present analytical results.

Larson, E. D.; Consonni, S.; Katofsky, R. E.; Iisa, K.; Frederick, W. J., Jr.

2008-11-01T23:59:59.000Z

256

Sensitivity of Utility-Scale Solar Deployment Projections in the SunShot Vision Study to Market and Performance Assumptions  

SciTech Connect

The SunShot Vision Study explored the potential growth of solar markets if solar prices decreased by about 75% from 2010 to 2020. The ReEDS model was used to simulate utility PV and CSP deployment for this present study, based on several market and performance assumptions - electricity demand, natural gas prices, coal retirements, cost and performance of non-solar renewable technologies, PV resource variability, distributed PV deployment, and solar market supply growth - in addition to the SunShot solar price projections. This study finds that utility-scale solar deployment is highly sensitive to solar prices. Other factors can have significant impacts, particularly electricity demand and natural gas prices.

Eurek, K.; Denholm, P.; Margolis, R.; Mowers, M.

2013-04-01T23:59:59.000Z

257

Sources of Corn for Ethanol Production in the United States: A Review and Decomposition Analysis of the Empirical Data  

Science Conference Proceedings (OSTI)

The use of corn for ethanol production in the United States quintupled between 2001 and 2009, generating concerns that this could lead to the conversion of forests and grasslands around the globe, known as indirect land-use change (iLUC). Estimates of iLUC and related food versus fuel concerns rest on the assumption that the corn used for ethanol production in the United States would come primarily from displacing corn exports and land previously used for other crops. A number of modeling efforts based on these assumptions have projected significant iLUC from the increases in the use of corn for ethanol production. The current study tests the veracity of these assumptions through a systematic decomposition analysis of the empirical data from 2001 to 2009. The logarithmic mean divisia index decomposition method (Type I) was used to estimate contributions of different factors to meeting the corn demand for ethanol production. Results show that about 79% of the change in corn used for ethanol production can be attributed to changes in the distribution of domestic corn consumption among different uses. Increases in the domestic consumption share of corn supply contributed only about 5%. The remaining contributions were 19% from added corn production, and 2% from stock changes. Yield change accounted for about two-thirds of the contributions from production changes. Thus, the results of this study provide little support for large land-use changes or diversion of corn exports because of ethanol production in the United States during the past decade.

Oladosu, Gbadebo A [ORNL; Kline, Keith L [ORNL; Uria Martinez, Rocio [ORNL; Eaton, Laurence M [ORNL

2011-01-01T23:59:59.000Z

258

Effect of oil shale type and retorting atmosphere on the products from retorting various oil shales by the controlled-state retort  

DOE Green Energy (OSTI)

Six oil shales from different locations (the Green River formation of Colorado and Utah, the Antrim Basin of Michigan, and Morocco) having different Fischer Assay oil yields were retorted using three retorting atmospheres (N/sub 2/, N/sub 2//steam, and N/sub 2//steam/O/sub 2/) under the same retorting conditions. The products (oils, gases, waters, and shales) were analyzed and the data are reported. Changing retorting atmospheres had little observable effect on the product oils; however, there was a great deal of change in the composition and amount of gas produced. Steam in the retorting atmosphere increased the amount of carbon dioxide produced and decreased the amount of carbonate and organic carbon in the retorted shale. Addition of oxygen to N/sub 2//steam and increasing the maximum temperature compounded the above effect. 3 figures, 20 tables.

Duvall, J.J.; Mason, K.K.

1980-02-01T23:59:59.000Z

259

TYPE OF OPERATION R Research & Development T& Facility Type  

Office of Legacy Management (LM)

-- R Research & Development T& Facility Type 0 Production scale testing a Pilat scale Y-. Bench Scale Process i Theoretical Studies Sample & Analysis 0 Productian 0 Disposal...

260

Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results  

E-Print Network (OSTI)

Homes End-Use Equipment Type Equipment Market Shares Index Heating ElecFurnace Gas Furnace LPG Furnace OilHomes (millions) End-Use Equipment Type Appliance stock in millions of units Index Heating FJec Furnace Gas Furnace L P G Furnace OilHomes End-Use Equipment Type Units Efficiency for Stock Equipment Index Heating Elec Furnace Btu.out/Wh.in Gas Furnace AFUE LPG Furnace AFUE Oil

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


261

Forecast Technical Document Forecast Types  

E-Print Network (OSTI)

Forecast Technical Document Forecast Types A document describing how different forecast types are implemented in the 2011 Production Forecast system. Tom Jenkins Robert Matthews Ewan Mackie Lesley Halsall #12;PF2011 ­ Forecast Types Background Different `types' of forecast are possible for a specified area

262

Similarities and distinctions of defect production by fast electron and proton irradiation: Moderately doped silicon and silicon carbide of n-type  

SciTech Connect

Effects of irradiation with 0.9 MeV electrons as well as 8 and 15 MeV protons on moderately doped n-Si grown by the floating zone (FZ) technique and n-SiC (4H) grown by chemical vapor deposition are studied in a comparative way. It has been established that the dominant radiation-produced defects with involvement of V group impurities differ dramatically in electron- and proton-irradiated n-Si (FZ), in spite of the opinion on their similarity widespread in literature. This dissimilarity in defect structures is attributed to a marked difference in distributions of primary radiation defects for the both kinds of irradiation. In contrast, DLTS spectra taken on electron- and proton-irradiated n-SiC (4H) appear to be similar. However, there are very much pronounced differences in the formation rates of radiation-produced defects. Despite a larger production rate of Frenkel pairs in SiC as compared to that in Si, the removal rates of charge carriers in n-SiC (4H) were found to be considerably smaller than those in n-Si (FZ) for the both electron and proton irradiation. Comparison between defect production rates in the both materials under electron and proton irradiation is drawn.

Emtsev, V. V., E-mail: emtsev@mail.ioffe.ru; Ivanov, A. M. [Russian Academy of Sciences, Ioffe Physicotechnical Institute (Russian Federation); Kozlovski, V. V. [St. Petersburg State Polytechnic University (Russian Federation); Lebedev, A. A.; Oganesyan, G. A.; Strokan, N. B. [Russian Academy of Sciences, Ioffe Physicotechnical Institute (Russian Federation); Wagner, G. [Leibniz-Institute for Crystal Growth (Germany)

2012-04-15T23:59:59.000Z

263

Buoyant Production and Consumption of Turbulence Kinetic Energy in Cloud-Topped Mixed Layers  

Science Conference Proceedings (OSTI)

Entrainment closure theories for mixed-layer models entail assumptions about how the net rate of buoyant production of turbulence kinetic energy is partitioned into gross production and consumption. Three alternative partitioning theories are ...

David A. Randall

1984-02-01T23:59:59.000Z

264

EERE Publication and Product Library  

NLE Websites -- All DOE Office Websites (Extended Search)

ANDOR select additional criteria (Information For, Information Type, Product Type, or EERE Office Name) to narrow your search results, AND PRESS 'SEARCH'. Search the Library...

265

Residential applliance data, assumptions and methodology for end-use forecasting with EPRI-REEPS 2.1  

Science Conference Proceedings (OSTI)

This report details the data, assumptions and methodology for end-use forecasting of appliance energy use in the US residential sector. Our analysis uses the modeling framework provided by the Appliance Model in the Residential End-Use Energy Planning System (REEPS), which was developed by the Electric Power Research Institute. In this modeling framework, appliances include essentially all residential end-uses other than space conditioning end-uses. We have defined a distinct appliance model for each end-use based on a common modeling framework provided in the REEPS software. This report details our development of the following appliance models: refrigerator, freezer, dryer, water heater, clothes washer, dishwasher, lighting, cooking and miscellaneous. Taken together, appliances account for approximately 70% of electricity consumption and 30% of natural gas consumption in the US residential sector. Appliances are thus important to those residential sector policies or programs aimed at improving the efficiency of electricity and natural gas consumption. This report is primarily methodological in nature, taking the reader through the entire process of developing the baseline for residential appliance end-uses. Analysis steps documented in this report include: gathering technology and market data for each appliance end-use and specific technologies within those end-uses, developing cost data for the various technologies, and specifying decision models to forecast future purchase decisions by households. Our implementation of the REEPS 2.1 modeling framework draws on the extensive technology, cost and market data assembled by LBL for the purpose of analyzing federal energy conservation standards. The resulting residential appliance forecasting model offers a flexible and accurate tool for analyzing the effect of policies at the national level.

Hwang, R.J,; Johnson, F.X.; Brown, R.E.; Hanford, J.W.; Kommey, J.G.

1994-05-01T23:59:59.000Z

266

The use of post detonation analysis of stable isotope ratios to determine the type and production process of the explosive involved  

SciTech Connect

The detonation of a series of explosives was performed in a controlled manner to collect the resulting, solid residue or {open_quotes}soot.{close_quotes} This residue was examined to determine the ratios of the stable carbon, hydrogen, and nitrogen isotopes. The goal of the experiment was to determine if these ratios could be used to indicate, from the post detonation residues, the type and origin of the detonated explosive. The ratios of the stated stable isotopes in the undetonated explosive were also determined. Despite some reservations in the quality of the data resulting from contamination by nonexplosive components, certain trends can be discerned. (1) Carbon isotopes allow aromatic explosives to be distinguished from nonaromatic explosives. This trend seems to carry through the detonation so that the distinction might be made after the fact. (2) The amination process for TATB can be detected through the hydrogen and, to some extent, the nitrogen isotope ratios. Unfortunately, the data are not sufficiently good to determine if this differential carries through the detonation. (3) The relative magnitude and sign of the nitrogen isotope ratio seems to carry through the detonation: some exchange with atmospheric nitrogen is probable. Even though this set of experiments must also be viewed as preliminary, there is a definite indication that certain qualitative characteristics of explosives can be detected after the detonation. This {open_quotes}signature{close_quotes} could have application to both intelligence and counter terrorism.

McGuire, R.R.; Velsko, C.A.; Lee, C.G.; Raber, E.

1993-03-05T23:59:59.000Z

267

Role of Outer Membrane C-Type Cytochromes MtrC and OmcA in Shewanella Oneidensis MR-1 Cell Production, Accumulation, and Detachment During Respiration on Hematite  

Science Conference Proceedings (OSTI)

Solid phase iron oxides are considered to be important terminal electron acceptors for microbial respiration in many anoxic environments. Besides the knowledge that cells attach to and reduce these substrates, other aspects of surface-associated cell behavior and the related cell surface components that influence cell-mineral interactions are not well understood. In the present study, wild-type cells of the dissimilatory iron-reducing bacterium Shewanella oneidensis MR-1 formed thin biofilms one-to-two cell layers in thickness when respiring on natural specular hematite under flow conditions similar to those which exist in aquatic sediments and subsurface environments. The distribution of cells within the biofilm indicated that direct contact was not required for electron transfer from cells to the mineral surface. Detached biomass in the form of single cells represented >99% of the surface-associated wild-type cell production from respiration on hematite over the biofilm life cycle. A mutant deficient in the outer membrane c35 type cytochrome OmcA, while still able to respire and replicate on hematite, established a lower steady-state cell density on the mineral surface than that of the wild-type strain. A mutant deficient in MtrC, another outer membrane c-type cytochrome, and a mutant deficient in both cytochromes were unable to reduce sufficient amounts of hematite to support detectable growth on the mineral surface. When considered in the context of previous work, the results support a growing body of evidence that the relative importance of OmcA and MtrC to cell respiration and replication depends on the form of iron oxide available as terminal electron acceptor.

Mitchell, Andrew C.; Peterson, L.; Reardon, Catherine L.; Reed, Samantha B.; Culley, David E.; Romine, Margaret F.; Geesey, Gill G.

2012-07-01T23:59:59.000Z

268

Modeling of Optimal Oil Production and Comparing with Actual and Contractual Oil Production: Iran Case  

E-Print Network (OSTI)

Modeling of Optimal Oil Production and Comparing with Actual and Contractual Oil Production: Iran, Davis Introduction · The Iran Oil Project, initiated in 2007, aims to find the inefficiencies and their possible sources in Iranian oil and gas policies. Background Information Assumptions · Perfect Competition

California at Davis, University of

269

This book adds an important nuance to the traditional historiographical assumption that trade in the Early Modern period was mostly conducted between family and those of the same  

E-Print Network (OSTI)

This book adds an important nuance to the traditional historiographical assumption that trade group. Rather, it is the assertion of this book, that there were very real and quite important trade relationships between merchants of different groups, and the book uses a case study of the Sephardim

van den Brink, Jeroen

270

Award Types  

NLE Websites -- All DOE Office Websites (Extended Search)

nuclear deterrent; reduce global threats; and solve other emerging national security and energy challenges. Contact Awards Team (505) 667-7824 Email Types of Awards The Awards...

271

Search for Pair Production of a Heavy Up-Type Quark Decaying to a W Boson and a b Quark in the lepton plus jets Channel with the ATLAS Detector  

Science Conference Proceedings (OSTI)

A search is presented for production of a heavy up-type quark (t{prime}) together with its antiparticle, assuming subsequent decay to a W boson and a b quark, t{prime}{bar t}{prime} {yields} W{sup +}bW{sup -}{bar b}. The search is based on 1.04 fb{sup -1} of proton-proton collisions at {radical}s = 7 TeV collected by the ATLAS detector at the CERN Large Hadron Collider. Data are analyzed in the lepton+jets final state, characterized by a high transverse momentum isolated electron or muon, high missing transverse momentum, and at least three jets. No significant excess of events above the background expectation is observed. A 95% C.L. lower limit of 404 GeV is set for the mass of the t{prime} quark.

Aad G.; Abbott, B.; Abdallah, J.; Abdelalim, A. A.; Abdesselam, A.; Abdinov, O.; Abi, B.; Abolins, M.; AbouZeid, O. S.; Abramowicz, H.; Abreu, H.; Acerbi, E.; Acharya, B. S.; Adamczyk, L.; Adams, D. L.; Addy, T. N.; Adelman, J.; Aderholz, M.; Adomeit, S.; et al.

2012-06-25T23:59:59.000Z

272

Federal Energy Management Program: Lighting Control Types  

NLE Websites -- All DOE Office Websites (Extended Search)

Lighting Control Lighting Control Types to someone by E-mail Share Federal Energy Management Program: Lighting Control Types on Facebook Tweet about Federal Energy Management Program: Lighting Control Types on Twitter Bookmark Federal Energy Management Program: Lighting Control Types on Google Bookmark Federal Energy Management Program: Lighting Control Types on Delicious Rank Federal Energy Management Program: Lighting Control Types on Digg Find More places to share Federal Energy Management Program: Lighting Control Types on AddThis.com... Energy-Efficient Products Federal Requirements Covered Product Categories Product Designation Process Low Standby Power Energy & Cost Savings Calculators Model Acquisitions Language Working Group Resources Technology Deployment Renewable Energy

273

Multimuon production in 280 GeV ?+ iron interactions  

Science Conference Proceedings (OSTI)

Results are presented on dimuon and trimuon final states in 280 GeV ?+ iron interactions. Both dimuon and trimuon data show clear evidence for open charm production and suggest strongly that the dominant production process is photon?gluon fusion. Similar amounts of elastic and inelastic (shower energy ?5 GeV) J/? production are measured in the trimuon sample. Elastic J/? production is consistent with photon?gluon fusion plus naive assumptions. Inelastic J/? production is inconsistent with this simple model

The European Muon Collaboration

1980-01-01T23:59:59.000Z

274

Kerosene-Type Jet Fuel Net Production  

U.S. Energy Information Administration (EIA)

-No Data Reported; --= Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Notes: Finished motor gasoline ...

275

TYPE OF OPERATION  

Office of Legacy Management (LM)

3!NEEi_S1 3!NEEi_S1 past: -~~~-~~~~~-~~~---------- current: ------------_------------- Owner contacted q yes g no; if ye=, date contacted TYPE OF OPERATION --~~__--~-~~~---- 5 Research & Development 5 Facility Type 0 Production scale testing c1 Pilot Scale 0 Bench Scale Process z Theareti cal Studi es Sample Sr Analysis 0 Production D Disposal/Storage TYPE OF CONTRACT ---------------- 0 Manufacturing 0 University 0 Research Clrganization B Government Cpanaored Faci 1 i ty 0 Other ~~---~~---_--~~-----_ a Prime 13 Subcontract& D PurcSase Order 0 Other information (i.e., cost + fixed fee, unit price, time & material, +z) ----_----------------------- Cantract/Purchaae Order #-d-z=&-/) -2_7~-------------Is_------------ PERIOD: CONTRACTING I%~(?) - 1465

276

A characterization of the Aerts product of Hilbertian lattices  

E-Print Network (OSTI)

Let H_1 and H_2 be complex Hilbert spaces, L_1=P(H_1) and L_2=P(H_2) the lattices of closed subspaces, and let L be a complete atomistic lattice. We prove under some weak assumptions relating L_i and L, that if L admits an orthocomplementation, then L is isomorphic to the separated product of L_1 and L_2 defined by Aerts. Our assumptions are minimal requirements for L to describe the experimental propositions concerning a compound system consisting of so called separated quantum systems. The proof does not require any assumption on the orthocomplementation of L.

Boris Ischi

2004-05-18T23:59:59.000Z

277

Melanin Types  

NLE Websites -- All DOE Office Websites (Extended Search)

Melanin Types Melanin Types Name: Irfan Location: N/A Country: N/A Date: N/A Question: What are different types of melanins? And what are the functions of these types? Replies: Hi Irfan! Melanin is a dark compound or better a photoprotective pigment. Its major role in the skin is to absorb the ultraviolet (UV) light that comes from the sun so the skin is not damaged. Sun exposure usually produces a tan at the skin that represents an increase of melanin pigment in the skin. Melanin is important also in other areas of the body, as the eye and the brain., but it is not completely understood what the melanin pigment does in these areas. Melanin forms a special cell called melanocyte. This cell is found in the skin, in the hair follicle, and in the iris and retina of the eye.

278

Type systems  

Science Conference Proceedings (OSTI)

The study of type systems has emerged as one of the most active areas of research in programming languages, with applications in software engineering, language design, high-performance compiler implementation, and security. This chapter discusses the ...

Benjamin C. Pierce

2003-01-01T23:59:59.000Z

279

TYPE OF OPERATION  

Office of Legacy Management (LM)

_---------_-- _---------_-- Research & Development 0 Production scale testing Cl Pilat Scale 0 Bench Scale Process 0 Theoretical Studies Cl Sample SC Analysis !J Production 0 Dis.posal/Storage 0 Prime ." 0 Subcontract& 0 Purchase Order 0 Facility Type 0 Manufacturing 0 University 0 Research Org&ization 0 Government Sponsored Facility Cl Other ---------_---__-____- Other information (i.e., cost + fixed fee, unit price, time & material, gtr) Coni+act/Purchase Order # ---------------------_--_________ C!2kEE~_CIL_N_G-EE~LE~: /5J--L-,r4 53 -------------------------------------- OWNERSHIP: AEC/MED AEC/MED GOVT GOVT CONTRACTOR CONTRACTOR !w!!E? ___--- " EWNED LEASED L_EesEE OWNED LEASED ---------_ --_------ LANDS BUILDINGS ' EQUIPMENT

280

TYPE OF OPERATION  

Office of Legacy Management (LM)

~~__--------_____ ~~__--------_____ q Research & Development q Production scale testing Cl Pilat Scale 0 Bench Scale Process 0 Theoretical Studies a Sample & Analysis c] Production 0 Disposal/Storage TYPE OF CONTRACT ~~__-------_--__ 0 Prime 0 Subcontractor 0 Purchase Order a d//F- a Faci 1 i ty Type a tlanuf acturi ng 0 University q Research Organization 0 Government Sponsored Facility a other --------------__----- Other information (i.e., cost + fixed fee, unit price, time & material, qtr) ------- -1------------------_L______ Contract/Purchase Order # CONTRACTING PE?IOD- 42 --------------L---- --------- ----------------_---______ OWNERSHIP: AEC/MED AEC/tlED OWNED ----- LE_A_sEE GOUT GO' JT CONTRACTOR E!!!!E!z LEASED - ----_ ---_OW_E!L LANDS BUILDINGS

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


281

TYPE OF OPERATION  

Office of Legacy Management (LM)

Owner c:ontacted Owner c:ontacted TYPE OF OPERATION ----------------_ jJ Research & Development 0 Production scale testing Cl Pilot Scale 0 Bench Scale Process i Theoretical Studies Sample & Analysis B Production 0 Disposal/Storage $r Prime 0 Subcontract& 0 Purchase Order 0 Facility Type 0 Manufacturing 0 University 0 Research Organization a Other information (i.e., cost + fixed fern, unit price,' time & mate ~r~~-r~~tf~-_~_-_~-~f-~~J~ d ial, etc)_kl/Jlfits ---- -7---- -- Contract/Purchase Order # w?@7-e?-b $ 6, i;,_~~~~~----------------- - ----- C_O!!IF!KXYE-PEELEg: -lTlL-/L?~J --------------------------- OWNERSHXP: AEWHEC AEC/HED' GOVT GB' JT SiXiRACTOR CONiRkCiGR WEE LEAs_EE a!!!%? IEEE!? --------_ ..---LEASED ._ OWNED LANDS BUILDINGS EQUIPMENT

282

Fuel Ethanol Total Stocks Stocks by Type  

U.S. Energy Information Administration (EIA)

Stock Type: Download Series History: Definitions, Sources & Notes: Show Data By: Product: Stock Type: Area: Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 View History; U ...

283

TYPE OF OPERATION  

Office of Legacy Management (LM)

______ ______ 0 Research & Development 9 Faciiity Type 0 Production scale testing Cl Pilot Scale 0 Bench Scale Process 0 Theoretical Studies Cl Sample 84 Analysis Production Di aposal /Storage g ;E:"V',;=:;;';"" IJ Research Organization 0 Government Sponeored Facility q Other --------------------- 0 Prime q ,@ Subcontract& Other information (i.e., cost 0 Purchase Order + fixed fee, unit price, time ?8 material, etc) -------mm----+------------- Contract/Purchase Order # CONTRACTING PERIODr c&L&.& rqs-z i i -----~_--~~~_----_ -------------------------------------- OWNERSHIP8 CIEC/tlED CIEC/MED GOUT WNED LE&xU _o!!EED LANDS BUILDINGS EQUIPMENT iii E : ORE OR RAW MATL IJ : E FINCIL PRODUCT [7 WCISTE b RESIDUE q GOUT

284

Recent developments in heavy flavour production  

E-Print Network (OSTI)

We review one-particle inclusive production of heavy-flavoured hadrons in a framework which resums the large collinear logarithms through the evolution of the FFs and PDFs and retains the full dependence on the heavy-quark mass without additional theoretical assumptions. We focus on presenting results for the inclusive cross section for the production of charmed mesons in p anti-p collisions and the comparison with CDF data from the Tevatron as well as on inclusive B-meson production and comparison with recent CDF data. The third topic is the production of D^* mesons in photoproduction and comparison with recent H1 data from HERA.

G. Kramer

2007-07-12T23:59:59.000Z

285

D:\assumptions_2001\assumptions2002\currentassump\demand.vp  

Gasoline and Diesel Fuel Update (EIA)

2 2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Household Expenditures Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Commercial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Natural Gas Transmission and Distribution Module . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Petroleum Market Module. . . . . . . . . . . . .

286

Type: Renewal  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

1 INCITE Awards 1 INCITE Awards Type: Renewal Title: -Ab Initio Dynamical Simulations for the Prediction of Bulk Properties‖ Principal Investigator: Theresa Windus, Iowa State University Co-Investigators: Brett Bode, Iowa State University Graham Fletcher, Argonne National Laboratory Mark Gordon, Iowa State University Monica Lamm, Iowa State University Michael Schmidt, Iowa State University Scientific Discipline: Chemistry: Physical INCITE Allocation: 10,000,000 processor hours Site: Argonne National Laboratory Machine (Allocation): IBM Blue Gene/P (10,000,000 processor hours) Research Summary: This project uses high-quality electronic structure theory, statistical mechanical methods, and

287

Hydrogen Production Infrastructure Options Analysis  

NLE Websites -- All DOE Office Websites (Extended Search)

Production Production Infrastructure Options Analysis January 26, 2006 Brian D. James Julie Perez Peter Schmidt (703) 243 - 3383 Brian_James@DirectedTechnologies.com Directed Technologies, Inc. Page 1 of 39 26 January 2006 2006-1-26 DOE Transition Workshop Agenda 1. Project Description and Objective 2. Team Members 3. Approach 4. Model Theory, Structure and Assumptions 5. Model Description 1. Logic 2. Features 3. Cost Components (Production, Delivery & Dispensing) 6. Los Angeles Transitional Example 7. Model Flexibility Page 2 of 39 26 January 2006 2006-1-26 DOE Transition Workshop Team Members & Interactions Start: May 2005 (effective) End: Summer 2007 * Directed Technologies, Inc.- Prime * Sentech, Inc., Research Partner * Air Products, Industrial Gas Supplier * Advisory Board * Graham Moore, Chevron Technology Ventures

288

Bacteria Types  

NLE Websites -- All DOE Office Websites (Extended Search)

Bacteria Types Bacteria Types Name: Evelyn Location: N/A Country: N/A Date: N/A Question: What is the significance of S. marcescens,M.luteus, S.epidermidis, and E. Coli? Which of these are gram-positive and gram-negative, and where can these be found? Also, what problems can they cause? When we culture these bacteria, we used four methods: plates, broth, slants, and pour plates. The media was made of TSB, TSA, NAP, and NAD. What is significant about these culturing methods? Replies: I could give you the answer to that question but it is more informative, and fun, to find out yourself. Start with the NCBI library online (http://www.ncbi.nlm.nih.gov/) and do a query with the species name, and 'virulence' if you want to know what they're doing to us. Have a look at the taxonomy devision to see how they are related. To find out if they're gram-pos or neg you should do a gram stain if you can. Otherwise you'll find that information in any bacteriology determination guide. Your question about the media is not specific enough so I can't answer it.

289

Productivity index of multilateral wells.  

E-Print Network (OSTI)

??In the history of petroleum science there are a vast variety of productivity solutions for different well types, well configurations and flow regimes. The main… (more)

Nunsavathu, Upender Naik.

2006-01-01T23:59:59.000Z

290

Atmospheric Responses of Gill-Type and Lindzen–Nigam Models to Global Warming  

Science Conference Proceedings (OSTI)

The equatorial Pacific atmosphere responds differently to global warming in the Gill-type and Lindzen–Nigam models. Under an assumption of no change in the zonal sea surface temperature (SST) gradient in the Gill-type model, the Walker circulation ...

Soon-Il An

2011-12-01T23:59:59.000Z

291

Assessment of Plutonium-238 (Pu-238) Production Alternatives  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Plutonium-238 Plutonium-238 Production Alternatives Briefing for Nuclear Energy Advisory Committee April 21, 2008 Dennis Miotla Deputy Assistant Secretary for Nuclear Power Deployment Miotla - April 2008 NEAC Mtg - DM183874 (2) Statement of Work Desired end state: - Reliable, sustainable, affordable supply of Pu-238 suitable for NASA applications Assumptions: - NASA obtains funding for planned missions - Russia is out of material to sell to US - DOE maintains balance of radioisotope power source infrastructure during period of depleted supply Independently evaluate the Pu-238 heat source requirements for NASA's mission projections and assess Pu-238 production assumptions, strategy and alternatives for meeting those requirements Miotla - April 2008 NEAC Mtg - DM183874 (3)

292

Glass Production  

E-Print Network (OSTI)

40, pp. 162 - 186. Glass Production, Shortland, UEE 2009AINES Short Citation: Shortland 2009, Glass Production. UEE.Andrew, 2009, Glass Production. In Willeke Wendrich (ed. ),

Shortland, Andrew

2009-01-01T23:59:59.000Z

293

Production Targets  

E-Print Network (OSTI)

Hall (2005), “Prices, Production, and Inventories over theProduction Targets ? Guillermo Caruana CEMFI caruana@cem?.esthe theory using monthly production targets of the Big Three

Caruana, Guillermo; Einav, Liran

2005-01-01T23:59:59.000Z

294

Pottery Production  

E-Print Network (OSTI)

Paul T. Nicholson. ) Pottery Production, Nicholson, UEE 2009Short Citation: Nicholson 2009, Pottery Production. UEE.Paul T. , 2009, Pottery Production. In Willeke Wendrich (

Nicholson, Paul T.

2009-01-01T23:59:59.000Z

295

Cordage Production  

E-Print Network (OSTI)

294: fig. 15-3). Cordage Production, Veldmeijer, UEE 2009Short Citation: Veldmeijer, 2009, Cordage Production. UEE.André J. , 2009, Cordage Production. In Willeke Wendrich (

Veldmeijer, André J.

2009-01-01T23:59:59.000Z

296

Microalgae Production Cost Analysis: Development of Goals And Its Implications On Future Research  

DOE Green Energy (OSTI)

This paper presents an overview of the production and economic models, with specific discussion of input assumptions used to derive microalgae product costs for the state of the art, theoretical-best and for the 1994 attainability target. These product cost estimates form the basis for developing program cost goals for microalgae fuel technology.

Hill, A. M.; McIntosh, R. P.

1984-01-17T23:59:59.000Z

297

Assumptions and Criteria for Performing a Feasability Study of the Conversion of the High Flux Isotope Reactor Core to Use Low-Enriched Uranium Fuel  

SciTech Connect

A computational study will be initiated during fiscal year 2006 to examine the feasibility of converting the High Flux Isotope Reactor from highly enriched uranium fuel to low-enriched uranium. The study will be limited to steady-state, nominal operation, reactor physics and thermal-hydraulic analyses of a uranium-molybdenum alloy that would be substituted for the current fuel powder--U{sub 3}O{sub 8} mixed with aluminum. The purposes of this document are to (1) define the scope of studies to be conducted, (2) define the methodologies to be used to conduct the studies, (3) define the assumptions that serve as input to the methodologies, (4) provide an efficient means for communication with the Department of Energy and American research reactor operators, and (5) expedite review and commentary by those parties.

Primm, R.T., III; Ellis, R.J.; Gehin, J.C.; Moses, D.L.; Binder, J.L.; Xoubi, N. (U. of Cincinnati)

2006-02-01T23:59:59.000Z

298

Hydrogen Production  

NLE Websites -- All DOE Office Websites (Extended Search)

Hydrogen Production DELIVERY FUEL CELLS STORAGE PRODUCTION TECHNOLOGY VALIDATION CODES & STANDARDS SYSTEMS INTEGRATION ANALYSES SAFETY EDUCATION RESEARCH & DEVELOPMENT Economy...

299

TYPE OF OPERATION  

Office of Legacy Management (LM)

----------------- ----------------- 0 Research & Development .a Production scale testing 0 Pilat Scale 0 Bench Scale Process 0 Thearetical Studies Cl Sample 84 Analysis 0 Production *i DiaposalKitorage Cl Facility Tybe q Government Sponsored Facility Other R.L- 6:e 14 1 1 ---------- --------- I I I TYPE OF CONTRACT ~-__-----------_ fl Prime *I 0 Subcantractbr Other infuriation (i.e., L.t + fixed fee, kit price, 0 Purchase Order time k mat*iik, gtc) /I -~---------'-t-----------~- ----------II---------------- Contract/Purchase Order # I EP!EBEII!G-PEEI9E: ---------------------------------~---- , OWNERSHiP: : I I j ,' / 1 AEC/tlED AEC/MED GOUT GOUT E!!NE_D LEASEI! !z%!NE_D CONTTACTOR CONTf?qCTOR LEASE?? ---w!En- ---LEL3SEI! i I I I LANDS BUILDINGS EIXIIPMENT

300

TYPE OF OPERATION  

Office of Legacy Management (LM)

OWNEF? (S) OWNEF? (S) Current: ____ LcrcJksLG! _________ Owner contacted n yes WI-IO; if yes, date contacted-- TYPE OF OPERATION ----_-------_---- m Research & Development Cl Pilot Scale Cl Disposal/Storaqe TYPE OF CDNTRACT ---__------__--- q Prime 0 Subcnntractor Cl Purchase Order 0 Other infcrmation (i.e., cnst + fixed fee, unit price, time 84 materi+, e.tc) v-7Y07-&G-W ---------------------------- Contract/Pur&aae Order # 0 -?+7- FJc-(CL --___--------~----_______________ CONTRACTING PEXIOD: fl& ,&I;'"'-?;': (&e-?)_-- ' ------------------ OWNERSHIP: AEC/MED GEC/MED SOVT GOVT CONTRACTOR CCNTRACTOR OWNE3 LEASE3 OWNE3 LEASED OWNE3 ----- ------ ----- ------ -__------- LE.352 LANDS u u q BUILDINGS EQUIPMENT 0 FINAL PRODUCT WASTE G RESIDUE a

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


301

Number: 1894 Type: factoid ...  

Science Conference Proceedings (OSTI)

... type> Type: factoid Description: How high is the pitcher's mound? ... 2047 Type: factoid Description: How close is Mercury to ...

2003-08-04T23:59:59.000Z

302

Mosaic neurofibromatosis type 1  

E-Print Network (OSTI)

with neurofibromatosis type 1 (NF1) with microdeletionsM, Huson S. Mosaic (segmental) neurofibromatosis type 1and type 2: no longer neurofibromatosis type 5. Am J Med

Liang, Christine; Schaffer, Julie V

2008-01-01T23:59:59.000Z

303

Indication of anomalous heat energy production in a reactor device  

E-Print Network (OSTI)

An experimental investigation of possible anomalous heat production in a special type of reactor tube named E-Cat HT is carried out. The reactor tube is charged with a small amount of hydrogen loaded nickel powder plus some additives. The reaction is primarily initiated by heat from resistor coils inside the reactor tube. Measurement of the produced heat was performed with high-resolution thermal imaging cameras, recording data every second from the hot reactor tube. The measurements of electrical power input were performed with a large bandwidth three-phase power analyzer. Data were collected in two experimental runs lasting 96 and 116 hours, respectively. An anomalous heat production was indicated in both experiments. The 116-hour experiment also included a calibration of the experimental set-up without the active charge present in the E-Cat HT. In this case, no extra heat was generated beyond the expected heat from the electric input. Computed volumetric and gravimetric energy densities were found to be far above those of any known chemical source. Even by the most conservative assumptions as to the errors in the measurements, the result is still one order of magnitude greater than conventional energy sources.

Giuseppe Levi; Evelyn Foschi; Torbjörn Hartman; Bo Höistad; Roland Pettersson; Lars Tegnér; Hanno Essén

2013-05-16T23:59:59.000Z

304

The Impact of Size Distribution Assumptions in a Bulk One-Moment Microphysics Scheme on Simulated Surface Precipitation and Storm Dynamics during a Low-Topped Supercell Case in Belgium  

Science Conference Proceedings (OSTI)

In this research the impact of modifying the size distribution assumptions of the precipitating hydrometeors in a bulk one-moment microphysics scheme on simulated surface precipitation and storm dynamics has been explored for long-lived low-topped ...

Kwinten Van Weverberg; Nicole P. M. van Lipzig; Laurent Delobbe

2011-04-01T23:59:59.000Z

305

Survey and forecast of marketplace supply and demand for energy- efficient lighting products  

SciTech Connect

The rapid growth in demand for energy-efficient lighting products has led to supply shortages for certain products. To understand the near-term (1- to 5-year) market for energy-efficient lighting products, a selected set of utilities and lighting product manufacturers were surveyed in early 1991. Two major U. S. government programs, EPA's Green Lights and DOE's Federal Relighting Initiative, were also examined to assess their effect on product demand. Lighting product manufacturers predicted significant growth through 1995. Lamp manufacturers indicated that compact fluorescent lamp shipments tripled between 1988 and 1991, and predicted that shipments would again triple, rising from 25 million units in 1991 to 72 million units in 1995. Ballast manufacturers predicted that demand for power-factorcorrected ballasts (both magnetic and electronic) would grow from 59.4 million units in 1991 to 71.1 million units in 1995. Electronic ballasts were predicted to grow from 11% of ballast demand in 1991 to 40% in 1995. Manufacturers projected that electronic ballast supply shortages would continue until late 1992. Lamp and ballast producers indicated that they had difficulty in determining what additional supply requirements might result due to demand created by utility programs. Using forecasts from 27 surveyed utilities and assumptions regarding the growth of U. S. utility lighting DSM programs, low, median, and high forecasts were developed for utility expenditures for lighting incentives through 1994. The projected median figure for 1992 was $316 million, while for 1994, the projected median figure was $547 million. The allocation of incentive dollars to various products and the number of units needed to meet utility-stimulated demand were also projected. To provide a better connection between future supply and demand, a common database is needed that captures detailed DSM program information including incentive dollars and unit-volume mix by product type.

Gough, A. (Lighting Research Inst., New York, NY (United States)); Blevins, R. (Plexus Research, Inc., Donegal, PA (United States))

1992-12-01T23:59:59.000Z

306

LBL-34046 UC-350 Residential Appliance Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1  

E-Print Network (OSTI)

This report details the data, assumptions and methodology for end-use forecasting of appliance energy use in the U.S. residential sector. Our analysis uses the modeling framework provided by the Appliance Model in the Residential End-Use Energy Planning System (REEPS), which was developed by the Electric Power Research Institute (McMenamin et al. 1992). In this modeling framework, appliances include essentially all residential end-uses other than space conditioning end-uses. We have defined a distinct appliance model for each end-use based on a common modeling framework provided in the REEPS software. This report details our development of the following appliance models: refrigerator, freezer, dryer, water heater, clothes washer, dishwasher, lighting, cooking and miscellaneous. Taken together, appliances account for approximately 70 % of electricity consumption and 30 % of natural gas consumption in the U.S. residential sector (EIA 1993). Appliances are thus important to those residential sector policies or programs aimed at improving the efficiency of electricity and natural gas consumption. This report is primarily methodological in nature, taking the reader through the entire process of developing the baseline for residential appliance end-uses. Analysis steps documented in this report include: gathering technology and market data for each appliance end-use and specific

J. Hwang; Francis X. Johnson; Richard E. Brown; James W. Hanford; Jonathan G. Koomey

1994-01-01T23:59:59.000Z

307

CAPITAL AND OPERATING COST OF HYDROGEN PRODUCTION FROM COAL GASIFICATI...  

NLE Websites -- All DOE Office Websites (Extended Search)

8 Coal Using Preliminary Assumptions 2-15 2.5.1 Approach to Cost Estimating 2-16 2.5.2 Production Costs (Operation and Maintenance) 2-16 2.5.3 Consumables 2-17 2.5.4 Byproduct...

308

Coal production: 1980  

Science Conference Proceedings (OSTI)

US coal production and related data are reported for the year 1980, with similar data for 1979 given for comparison. The data here collected on Form EIA-7A, coal production report, from 3969 US mines that produced, processed, or prepared 10,000 or more short tons of coal in 1980. Among the items covered are production, prices, employment, productivity, stocks, and recoverable reserves. Data are reported by state, county, coal producing district, type of mining, and by type of coal (anthracite, bituminous, subbituminous, and lignite). Also included are a glossary of coal terms used, a map of the coal producing disricts, and form EIA-7A with instructions. 14 figures, 63 tables.

Not Available

1982-05-01T23:59:59.000Z

309

Type checking and normalisation.  

E-Print Network (OSTI)

??This thesis is about Martin-Löf's intuitionistic theory of types (type theory). Type theory is at the same time a formal system for mathematical proof and… (more)

Chapman, James Maitland

2009-01-01T23:59:59.000Z

310

Hybrid type checking  

E-Print Network (OSTI)

Phase distinctions in type theory. Manuscript, 1988. [10]Typechecking dependent types and subtypes. In Lecture notesF. Pfenning. Intersection types and computational effects.

Flanagan, C

2006-01-01T23:59:59.000Z

311

Type 2 segmental glomangiomas  

E-Print Network (OSTI)

skin disorders: different types of severitiy reflectevidence for dichotomous types of severitiy. Arch Dermatol9. Happle R, König A. Type 2 segmental manifestation of

Hoekzema, Rick; Zonneveld, Ingrid M; Wal, Allard C van der

2010-01-01T23:59:59.000Z

312

OPEC Crude Oil Production 1999-2001  

Gasoline and Diesel Fuel Update (EIA)

EIA assumes in its base case that OPEC 10 production averages about EIA assumes in its base case that OPEC 10 production averages about 0.6 million barrels per day less in the 1st quarter of 2001 than was produced in the 4th quarter of 2000. This is based on the assumption that beginning in February 2001, OPEC 10 production is 1 million barrels per day less than the estimate for December 2000. Over the course of the past year, worldwide oil production has increased by about 3.7 million barrels per day to a level of 77.8 million barrels per day in the last months of 2000. After being nearly completely curtailed in December 2000, EIA's base case assumes that Iraqi oil exports only partially return in January. By February, EIA assumes Iraqi crude oil production reaches 3 million barrels per day, roughly the peak levels reached last year.

313

Writing with Complex Type  

E-Print Network (OSTI)

29] Middendorp, J. 2004. Dutch type. 010 Publishers. [30]A. Hyland. 1992. Twentieth-century type. Laurence King. [7]Robertson. 2005. From Movable Type to Moving Type-Evolution

Lewis, Jason; Nadeau, Bruno

2009-01-01T23:59:59.000Z

314

abstract data type  

Science Conference Proceedings (OSTI)

Definition of abstract data type, possibly with links to more information and implementations. NIST. abstract data type. (definition). ...

2013-11-08T23:59:59.000Z

315

Analysis on Modulation Principle of Mechanical Spring Valve Block-Type Pulse Jet  

Science Conference Proceedings (OSTI)

To take full advantage of the bottom-hole hydraulic energy to improve the drilling rate, it is proposed the technique assumption that using mechanical spring valve periodically is to block the fluid pathway, and modulating pulse jet is to increase the ... Keywords: block type, pulse jet, drilling rate, water hammer, experimental study

Ni Hongjian; Zhu Lihong; Huo Hongjun; Tang Zhiwen

2011-08-01T23:59:59.000Z

316

Programs on Product Spaces  

Science Conference Proceedings (OSTI)

We study program states that are described as tuples, i.e., product state spaces. We show how to add program variables and assignment notation to simply typed lambda calculus in order to describe functions, relations and predicate transformers on such ...

Ralph Back; Joakim Wright von

1997-12-01T23:59:59.000Z

317

MARINE BIOMASS SYSTEM: ANAEROBIC DIGESTION AND PRODUCTION OF METHANE  

E-Print Network (OSTI)

Performance Data For Anaerobic Digestion of Various Types ofMARINE BIOMASS SYSTEM: ANAEROBIC DIGESTION AND PRODUCTION OFMARINE BIOMASS SYSTEM: ANAEROBIC DIGESTION AND PRODUCTION OF

Haven, Kendall F.

2011-01-01T23:59:59.000Z

318

A Systems Framework for Assessing Plumbing Products-Related Water...  

NLE Websites -- All DOE Office Websites (Extended Search)

A Systems Framework for Assessing Plumbing Products-Related Water Conservation Title A Systems Framework for Assessing Plumbing Products-Related Water Conservation Publication Type...

319

RMOTC - Production  

NLE Websites -- All DOE Office Websites (Extended Search)

Production Production RMOTC Pumpjack in action During the process of the sale of NPR-3, RMOTC will focus on maximizing the value of the NPR-3 site and will continue with its Production Optimization Projects. NPR-3 includes 9,481 acres with more than 400 oil-producing wells. Current oil production is at approximately 240 barrels of oil per day. In July 2013, RMOTC began working on a number of Production Optimization Projects within the NPR-3 field, with the goal to optimize and improve flow and efficiency. Production Optimization Projects include repairing and replacing existing infrastructure with new infrastructure in order to optimize current wells and bring additional wells online. These Production Optimization Projects will continue throughout 2013 and are focused on improving current production and creating revenue for the America tax payer.

320

Antihydrogen production  

SciTech Connect

Antihydrogen production in ATHENA is analyzed more carefully. The most important peculiarities of the different experimental situations are discussed. The protonium production via the first matter-antimatter chemical reaction is commented too.

Rizzini, Evandro Lodi; Venturelli, Luca; Zurlo, Nicola [Dipartimento di Chimica e Fisica per l'Ingegneria e per i Materiali, Universita di Brescia, 25133 Brescia (Italy); Istituto Nazionale di Fisica Nucleare, Gruppo Collegato di Brescia, 25133 Brescia (Italy)

2008-08-08T23:59:59.000Z

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


321

Tin Production  

Science Conference Proceedings (OSTI)

...descending order, Brazil, Indonesia, Malaysia, Thailand, Bolivia, and Australia. These countries supply more than 85% of total world production....

322

Measuring impact of CONWIP control on production rate and inventory distribution for a flexible production system through simulation  

E-Print Network (OSTI)

This research investigates the production output and inventory distribution of a manufacturing system that produces multiple part types on flexible machines while incorporating CONWIP inventory controls. The production ...

Lampe, Erik Joseph

2007-01-01T23:59:59.000Z

323

On Energy and Entropy Influxes in the Green-Naghdi Type III Theory of Heat Conduction  

E-Print Network (OSTI)

The energy-influx/entropy-influx relation in the Green-Naghdi Type III theory of heat conduction is examined within a thermodynamical framework \\`a la Mueller-Liu, where that relation is not specified a priori irrespectively of the constitutive class under attention. It is shown that the classical assumption, i.e., that the entropy influx and the energy influx are proportional via the absolute temperature, holds true if heat conduction is, in a sense that is made precise, isotropic. In addition, it is proven that the standard assumption does not hold in case of transversely isotropic conduction.

Swantje Bargmann; Antonino Favata; Paolo Podio-Guidugli

2012-09-13T23:59:59.000Z

324

Bakken Shale Oil Production Trends  

E-Print Network (OSTI)

As the conventional reservoirs decrease in discovering, producing and reserving, unconventional reservoirs are more remarkable in terms of discovering, development and having more reserve. More fields have been discovered where Barnett Shale and Bakken Shale are the most recently unconventional reservoir examples. Shale reservoirs are typically considered self-sourcing and have very low permeability ranging from 10-100 nanodarcies. Over the past few decades, numerous research projects and developments have been studied, but it seems there is still some contention and misunderstanding surrounding shale reservoirs. One of the largest shale in the United State is the Bakken Shale play. This study will describe the primary geologic characteristics, field development history, reservoir properties,and especially production trends, over the Bakken Shale play. Data are available for over hundred wells from different companies. Most production data come from the Production Data Application (HDPI) database and in the format of monthly production for oil, water and gas. Additional 95 well data including daily production rate, completion, Pressure Volume Temperature (PVT), pressure data are given from companies who sponsor for this research study. This study finds that there are three Types of well production trends in the Bakken formation. Each decline curve characteristic has an important meaning to the production trend of the Bakken Shale play. In the Type I production trend, the reservoir pressure drops below bubble point pressure and gas releasingout of the solution. With the Type II production trend, oil flows linearly from the matrix into the fracture system, either natural fracture or hydraulic fracture. Reservoir pressure is higher than the bubble point pressure during the producing time and oil flows as a single phase throughout the production period of the well. A Type III production trend typically has scattering production data from wells with a different Type of trend. It is difficult to study this Type of behavior because of scattering data, which leads to erroneous interpretation for the analysis. These production Types, especially Types I and II will give a new type curve matches for shale oil wells above or below the bubble point.

Tran, Tan

2011-05-01T23:59:59.000Z

325

Alternative Fuels Data Center: Biofuels Production Incentive  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Biofuels Production Biofuels Production Incentive to someone by E-mail Share Alternative Fuels Data Center: Biofuels Production Incentive on Facebook Tweet about Alternative Fuels Data Center: Biofuels Production Incentive on Twitter Bookmark Alternative Fuels Data Center: Biofuels Production Incentive on Google Bookmark Alternative Fuels Data Center: Biofuels Production Incentive on Delicious Rank Alternative Fuels Data Center: Biofuels Production Incentive on Digg Find More places to share Alternative Fuels Data Center: Biofuels Production Incentive on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Biofuels Production Incentive The Mississippi Department of Agriculture and Commerce (Department) provides incentive payments to qualified ethanol and biodiesel producers

326

Type systems for dummies  

Science Conference Proceedings (OSTI)

We extend Pure Type Systems with a function turning each term M of type A into a dummy |M| of the same type (|.| is not an identity, in that M ? |M|). Intuitively, a dummy represents an unknown, canonical object of the given type: dummies are opaque ... Keywords: canonical element, proof irrelevance, pure type system

Andrea Asperti; Ferruccio Guidi

2012-01-01T23:59:59.000Z

327

OPEC Crude Oil Production 1999-2001  

Gasoline and Diesel Fuel Update (EIA)

9 9 Notes: EIA assumes in its base case that OPEC 10 production averages about 0.6 million barrels per day less in the 1st quarter of 2001 than was produced in the 4th quarter of 2000. This is based on the assumption that beginning in February 2001, OPEC 10 production is 1 million barrels per day less than the estimate for December 2000. From the fourth quarter of 1999 to the 4th quarter of 2000, worldwide oil production increased by about 3.7 million barrels per day to a level of 77.8 million barrels per day. After being sharply curtailed in December 2000, EIA's base case assumes that Iraqi oil exports only partially return in January. By February, EIA assumes Iraqi crude oil production reaches 3 million barrels per day, roughly the peak levels reached last year.

328

OPEC Crude Oil Production 1998-2001  

Gasoline and Diesel Fuel Update (EIA)

6 6 Notes: EIA assumes in its base case that OPEC 10 production averages about 0.6 million barrels per day less in the 1st quarter of 2001 than was produced in the 4th quarter of 2000. This is based on the assumption that beginning in February 2001, OPEC 10 production is 1 million barrels per day less than the estimate for December 2000. From the fourth quarter of 1999 to the 4th quarter of 2000, worldwide oil production increased by about 3.8 million barrels per day to a level of 77.9 million barrels per day. After being sharply curtailed in December and January, EIA's base case assumes that Iraqi oil exports return closer to more normal levels in February. By the second half of 2001, EIA assumes Iraqi crude oil production reaches 3 million barrels per day, roughly the peak levels

329

Ergodic distribution for a fuzzy inventory model of type (s,S) with gamma distributed demands  

Science Conference Proceedings (OSTI)

In this study, a stochastic process (X(t)), which describes a fuzzy inventory model of type (s,S) is considered. Under some weak assumptions, the ergodic distribution of the process X(t) is expressed by a fuzzy renewal function U(x). Then, membership ... Keywords: Ergodic distribution, Fuzzy inventory model of type (s,S), Fuzzy renewal function, Gamma distribution with fuzzy parameter

Tahir Khaniyev; I. Burhan Turksen; Fikri Gokpinar; Basak Gever

2013-02-01T23:59:59.000Z

330

Propane/Propylene Total Stocks Stocks by Type  

U.S. Energy Information Administration (EIA)

Stock Type: Download Series History: Definitions, Sources & Notes: Show Data By: Product: Stock Type: Area: Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 View History; U ...

331

Naphtha for Petrochemical Feedstock Use Total Stocks Stocks by Type  

U.S. Energy Information Administration (EIA)

Stock Type: Download Series History: Definitions, Sources & Notes: Show Data By: Product: Stock Type: Area: Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 View History; U ...

332

Propane/Propylene Natural Gas Processing Plant Stocks by Type  

U.S. Energy Information Administration (EIA)

Stock Type: Download Series History: Definitions, Sources & Notes: Show Data By: Product: Stock Type: Area: Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 View History; U ...

333

Residual Fuel Oil Bulk Terminal Stocks by Type  

U.S. Energy Information Administration (EIA)

Stock Type: Download Series History: Definitions, Sources & Notes: Show Data By: Product: Stock Type: Area: Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 View History; U ...

334

Asphalt and Road Oil Total Stocks Stocks by Type  

U.S. Energy Information Administration (EIA)

Stock Type: Download Series History: Definitions, Sources & Notes: Show Data By: Product: Stock Type: Area: Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 View History; U ...

335

ARM - Measurement - Cloud type  

NLE Websites -- All DOE Office Websites (Extended Search)

type ARM Data Discovery Browse Data Comments? We would love to hear from you Send us a note below or call us at 1-888-ARM-DATA. Send Measurement : Cloud type Cloud type such as...

336

production | OpenEI  

Open Energy Info (EERE)

production production Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 1, and contains only the reference case. The dataset uses quadrillion BTUs, and quantifies the energy prices using U.S. dollars. The data is broken down into total production, imports, exports, consumption, and prices for energy types. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO consumption EIA export import production reference case total energy Data application/vnd.ms-excel icon AEO2011: Total Energy Supply, Disposition, and Price Summary - Reference Case (xls, 112.8 KiB) Quality Metrics Level of Review Peer Reviewed

337

How much does it cost to generate electricity with different types ...  

U.S. Energy Information Administration (EIA)

Reserves, production, prices, employ- ment and productivity, distribution, ... How much does it cost to generate electricity with different types of power plants?

338

Topic: Productivity  

Science Conference Proceedings (OSTI)

... General Information: 301-975-5020 mfg@nist ... competitive in the global market, companies need to ... become more efficient in energy, production and ...

2013-09-26T23:59:59.000Z

339

Silicon Production  

Science Conference Proceedings (OSTI)

Mar 12, 2012 ... An Investigation into the Electrochemical Production of Si by the FFC Cambridge Process: Emre Ergül1; ?shak Karakaya2; Metehan Erdo?an2; ...

340

OIL PRODUCTION  

NLE Websites -- All DOE Office Websites (Extended Search)

OIL PRODUCTION Enhanced Oil Recovery (EOR) is a term applied to methods used for recovering oil from a petroleum reservoir beyond that recoverable by primary and secondary methods....

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


341

Hydrogen Production  

Office of Scientific and Technical Information (OSTI)

Hydrogen Production Hydrogen Research in DOE Databases Energy Citations Database Information Bridge Science.gov WorldWideScience.org Increase your H2IQ More information Making...

342

Type-checking injective pure type systems  

Science Conference Proceedings (OSTI)

Injective pure type systems form a large class of pure type systems for which one can compute by purely syntactic means two sorts elmt(?∣M) and sort(?∣M), where ? is a pseudo-context and M is a pseudo-term, ...

Gilles Barthe

1999-11-01T23:59:59.000Z

343

Product Classification  

Science Conference Proceedings (OSTI)

Table 1   Major types and uses of pipe...Uses Standard Industrial or residential water steam, oil, or gas

344

Typing constraint logic programs  

Science Conference Proceedings (OSTI)

We present a prescriptive type system with parametric polymorphism and subtyping for constraint logic programs. The aim of this type system is to detect programming errors statically. It introduces a type discipline for constraint logic programs and ... Keywords: Constraint logic programming, Metaprogramming, Prolog, subtyping, type systems

François Fages; Emmanuel Coquery

2001-11-01T23:59:59.000Z

345

AQUA Products | Open Energy Information  

Open Energy Info (EERE)

AQUA Products AQUA Products Jump to: navigation, search Name AQUA Products Place Prosperity, SC Zip 29127 Product Manufacturer of small tonnage chiller systems Year founded 1993 Website http://www.aquaproducts.us/ Coordinates 34.2093079°, -81.5331602° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":34.2093079,"lon":-81.5331602,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

346

Property:NrelPartnerType | Open Energy Information  

Open Energy Info (EERE)

NrelPartnerType NrelPartnerType Jump to: navigation, search Property Name NrelPartnerType Property Type String Description Partnership Type. Pages using the property "NrelPartnerType" Showing 25 pages using this property. (previous 25) (next 25) 1 1366 Technologies + Incubator + 3 3M + CRADA + A A.O. Smith + Test & Evaluation Partner + A123Systems + CRADA + AAON + Test & Evaluation Partner + AQUA Products + Test & Evaluation Partner + AVL Powertrain Engineering + Licensing Agreement + AWS Truewind + Test & Evaluation Partner + Abengoa Solar + CRADA + Abound Solar + Other Relationship + Advanced Energy Products + Test & Evaluation Partner + Affiliated International Management (AIM) + Test & Evaluation Partner + Affordable Comfort + Test & Evaluation Partner +

347

Entanglement Production in Non-Equilibrium Thermodynamics  

E-Print Network (OSTI)

We define and analyse the concept of entanglement production during the evolution of a general quantum mechanical dissipative system. While it is important to minimise entropy production in order to achieve thermodynamical efficiency, maximising the rate of change of entanglement is important in quantum information processing. Quantitative relations are obtained between entropy and entanglement productions, under specific assumptions detailed in the text. We apply these to the processes of dephasing and decay of correlations between two initially entangled qubits. Both the Master equation treatment as well as the higher Hilbert space analysis are presented. Our formalism is very general and contains as special cases many reported individual instance of entanglement dynamics, such as, for example, the recently discovered notion of the sudden death of entanglement.

V. Vedral

2007-06-21T23:59:59.000Z

348

Regular Object Types  

E-Print Network (OSTI)

Regular expression types have been proposed as a foundation for statically typed processing of XML and similar forms of tree-structured data. To date, however, regular expression types have been explored in special-purpose languages (e.g., XDuce, CDuce, and XQuery) with type systems designed around regular expression types "from the ground up." The goal of the Xtatic language is to bring regular expression types to a broad audience by offering them as a lightweight extension of a popular object-oriented language, C#. We develop...

Vladimir Gapeyev; Benjamin C. Pierce

2003-01-01T23:59:59.000Z

349

Hydrogen Production  

Fuel Cell Technologies Publication and Product Library (EERE)

This 2-page fact sheet provides a brief introduction to hydrogen production technologies. Intended for a non-technical audience, it explains how different resources and processes can be used to produ

350

Type-II intermittency characteristics in the presence of noise  

E-Print Network (OSTI)

We consider a type of intermittent behavior that occurs as the result of the interplay between dynamical mechanisms giving rise to type-II intermittency and random dynamics. We analytically deduce the law for the distribution of the laminar phases, which has never been obtained hitherto. The already known dependence of the mean length of the laminar phases on the criticality parameter [PRE 68 (2003) 036203] follows as a corollary of the carried out research. We also prove that this dependence obtained earlier under the assumption of the fixed form of the reinjection probability does not depend on the relaminarization properties, and, correspondingly, the obtained expression of the mean length of the laminar phases on the criticality parameter remains correct for different types of the reinjection probability.

Alexey A. Koronovskii; Alexander E. Hramov

2013-02-17T23:59:59.000Z

351

MEMORANDUM TO: FILE TYPE OF OPERATION  

Office of Legacy Management (LM)

TYPE OF OPERATION TYPE OF OPERATION _--__---~~--~---~ a Research & Development cl Facility Type 0 Production scale testing 0 Pilot Scale 0 Bench Scale Process 0 Theoretical Studies a Sample SC Analysis 0 Hanuf actuiing 0 University a Research Organization 0 Government Sponsored Facility 0 Other ~---~~--_--_~-___--~ 0 Production 0 Disposal/Storage IYPLPEs!b!Iw!EI 0 Prime a 0 Subcontract& Other information (i.e., cost + fixed fee. unit price, *! Purchase Order time & material, qtc) _------ -------------42-----__--_---- ContFact/Purchase Order # ud IdlijL1\^IIJ ---------------------------- --------------------------------- OWNERSHIP: GOUT GOVT CONTRACTOR -CONTRACTOR awED LE_ASED OWNED ---------- ~-~LE!sEn LANDS BUILDINGS EQUIPMENT ORE OR RAW MATL 0 FINAL PRODUCT 0

352

Types of Radiation Exposure  

NLE Websites -- All DOE Office Websites (Extended Search)

External Irradiation Contamination Incorporation Biological Effects of Acute, Total Body Irradiation Managing Radiation Emergencies Procedure Demonstration Types of radiation...

353

USDA Forest Products Laboratory | Open Energy Information  

Open Energy Info (EERE)

Forest Products Laboratory Forest Products Laboratory Jump to: navigation, search Name USDA Forest Products Laboratory Place Madison, WI Website http://www.fpl.fs.fed.us/ References USDA Forest Products Laboratory [1] Information About Partnership with NREL Partnership with NREL Yes Partnership Type Test & Evaluation Partner Partnering Center within NREL Electricity Resources & Building Systems Integration LinkedIn Connections CrunchBase Profile No CrunchBase profile. Create one now! USDA Forest Products Laboratory is a company located in Madison, WI. References ↑ "USDA Forest Products Laboratory" Retrieved from "http://en.openei.org/w/index.php?title=USDA_Forest_Products_Laboratory&oldid=381741" Categories: Clean Energy Organizations Companies Organizations

354

Type B Drum packages  

Science Conference Proceedings (OSTI)

The Type B Drum package is a container in which a single drum containing Type B quantities of radioactive material will be packaged for shipment. The Type B Drum containers are being developed to fill a void in the packaging and transportation capabilities of the US Department of Energy (DOE), as no double containment packaging for single drums of Type B radioactive material is currently available. Several multiple-drum containers and shielded casks presently exist. However, the size and weight of these containers present multiple operational challenges for single-drum shipments. The Type B Drum containers will offer one unshielded version and, if needed, two shielded versions, and will provide for the option of either single or double containment. The primary users of the Type B Drum container will be any organization with a need to ship single drums of Type B radioactive material. Those users include laboratories, waste retrieval facilities, emergency response teams, and small facilities.

Edwards, W.S.

1995-11-01T23:59:59.000Z

355

Type I Interferon is Not Just for Viruses: Cytosolic Sensing of Bacterial Nucleic Acids  

E-Print Network (OSTI)

and Oldstone, M.B. (2007). Type I interferon during viralCutting edge: FimH adhesin of type 1 fimbriae is a novelTaniguchi, T. , et al. (2004). Type I interferon production

Monroe, Kathryn McGee

2011-01-01T23:59:59.000Z

356

Assessment of the Impacts of Standards and Labeling Programs in Mexico (four products).  

E-Print Network (OSTI)

Three-Phase Electric Motors 4.3.1. Energy Savings 4.3.2.Energy Savings Compared to other products, electric motorsElectric motors: 20 years Clothes washers: 15 years Reference: Original assumption made by manufacturers Comments: For clothes washers we calculated the energy savings

Sanchez, Itha; Pulido, Henry; McNeil, Michael A.; Turiel, Isaac; della Cava, Mirka

2007-01-01T23:59:59.000Z

357

Alternative Fuels Data Center: Ethanol Production Incentive  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Ethanol Production Ethanol Production Incentive to someone by E-mail Share Alternative Fuels Data Center: Ethanol Production Incentive on Facebook Tweet about Alternative Fuels Data Center: Ethanol Production Incentive on Twitter Bookmark Alternative Fuels Data Center: Ethanol Production Incentive on Google Bookmark Alternative Fuels Data Center: Ethanol Production Incentive on Delicious Rank Alternative Fuels Data Center: Ethanol Production Incentive on Digg Find More places to share Alternative Fuels Data Center: Ethanol Production Incentive on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Ethanol Production Incentive The Ethanol Production Incentive provides qualified ethanol producers with quarterly payments based on production volume during times when ethanol

358

Refinery & Blender Net Production of Kerosene-Type Jet Fuel  

U.S. Energy Information Administration (EIA)

-No Data Reported; --= Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Notes: See Definitions ...

359

Crude Oil and Petroleum Products Total Stocks Stocks by Type  

U.S. Energy Information Administration (EIA)

-No Data Reported; --= Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Notes: Crude oil stocks in the ...

360

Crude Oil and Petroleum Products Bulk Terminal Stocks by Type  

U.S. Energy Information Administration (EIA)

-No Data Reported; --= Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Notes: Crude oil stocks in the ...

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


361

Product Supplied for Kerosene-Type Jet Fuel  

U.S. Energy Information Administration (EIA)

-No Data Reported; --= Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Notes: Data may not add to ...

362

Refinery Net Production of Kerosene-Type Jet Fuel - Commercial  

U.S. Energy Information Administration (EIA)

-No Data Reported; --= Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Notes: See Definitions ...

363

Kerosene-Type Jet Fuel Production - Energy Information Administration  

U.S. Energy Information Administration (EIA)

-No Data Reported; --= Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Notes: Beginning in 1993, motor ...

364

Hydrogen production  

SciTech Connect

The production of hydrogen by reacting an ash containing material with water and at least one halogen selected from the group consisting of chlorine, bromine and iodine to form reaction products including carbon dioxide and a corresponding hydrogen halide is claimed. The hydrogen halide is decomposed to separately release the hydrogen and the halogen. The halogen is recovered for reaction with additional carbonaceous materials and water, and the hydrogen is recovered as a salable product. In a preferred embodiment the carbonaceous material, water and halogen are reacted at an elevated temperature. In accordance with another embodiment, a continuous method for the production of hydrogen is provided wherein the carbonaceous material, water and at least one selected halogen are reacted in one zone, and the hydrogen halide produced from the reaction is decomposed in a second zone, preferably by electrolytic decomposition, to release the hydrogen for recovery and the halogen for recycle to the first zone. There also is provided a method for recovering any halogen which reacts with or is retained in the ash constituents of the carbonaceous material.

Darnell, A.J.; Parkins, W.E.

1978-08-08T23:59:59.000Z

365

Product Forms  

Science Conference Proceedings (OSTI)

Table 1 Wrought alloy products and tempers...or cold-finished Rivets Forgings and forging stock Foil Fin stock Drawn Extruded Rod Bar Wire 1050 . . . . . . . . . H112 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1060 O, H12, H14, H16, H18 O, H12, H14, H112 O, H12, H14, H18, H113 O, H112 . . . .

366

n-Linear Algebra of type II  

E-Print Network (OSTI)

This book is a continuation of the book n-linear algebra of type I and its applications. Most of the properties that could not be derived or defined for n-linear algebra of type I is made possible in this new structure: n-linear algebra of type II which is introduced in this book. In case of n-linear algebra of type II we are in a position to define linear functionals which is one of the marked difference between the n-vector spaces of type I and II. However all the applications mentioned in n-linear algebras of type I can be appropriately extended to n-linear algebras of type II. Another use of n-linear algebra (n-vector spaces) of type II is that when this structure is used in coding theory we can have different types of codes built over different finite fields whereas this is not possible in the case of n-vector spaces of type I. Finally in the case of n-vector spaces of type II, we can obtain n-eigen values from distinct fields; hence, the n-characteristic polynomials formed in them are in distinct different fields. An attractive feature of this book is that the authors have suggested 120 problems for the reader to pursue in order to understand this new notion. This book has three chapters. In the first chapter the notion of n-vector spaces of type II are introduced. This chapter gives over 50 theorems. Chapter two introduces the notion of n-inner product vector spaces of type II, n-bilinear forms and n-linear functionals. The final chapter suggests over a hundred problems. It is important that the reader is well-versed not only with linear algebra but also n-linear algebra of type I.

W. B. Vasantha Kandasamy; Florentin Smarandache

2009-02-01T23:59:59.000Z

367

Product Classification  

Science Conference Proceedings (OSTI)

Table 1 Major types and uses of pipe...or residential water steam, oil, or gas transmission; distribution or service lines; structural uses Special Conduit, piling, pipe for nipples, and other Line Oil- or gas-transmission pipe, water-main pipe Oil country tubular goods Drill pipe, casing, tubing Water well Drive pipe, driven-well pipe,...

368

Advanced Production Planning Models  

SciTech Connect

>This report describes the innovative modeling approach developed as a result of a 3-year Laboratory Directed Research and Development project. The overall goal of this project was to provide an effective suite of solvers for advanced production planning at facilities in the nuclear weapons complex (NWC). We focused our development activities on problems related to operations at the DOE's Pantex Plant. These types of scheduling problems appear in many contexts other than Pantex--both within the NWC (e.g., Neutron Generators) and in other commercial manufacturing settings. We successfully developed an innovative and effective solution strategy for these types of problems. We have tested this approach on actual data from Pantex, and from Org. 14000 (Neutron Generator production). This report focuses on the mathematical representation of the modeling approach and presents three representative studies using Pantex data. Results associated with the Neutron Generator facility will be published in a subsequent SAND report. The approach to task-based scheduling described here represents a significant addition to the literature for large-scale, realistic scheduling problems in a variety of production settings.

JONES,DEAN A.; LAWTON,CRAIG R.; KJELDGAARD,EDWIN A.; WRIGHT,STEPHEN TROY; TURNQUIST,MARK A.; NOZICK,LINDA K.; LIST,GEORGE F.

2000-12-01T23:59:59.000Z

369

TYPES OF FIELD TESTING  

NLE Websites -- All DOE Office Websites (Extended Search)

TYPES OF FIELD TESTING Convincing proof of energy savings and performance in a specific building and occupant context If direct proof of savings is desired, the only feasible...

370

Perspectives on distributed computing : thirty people, four user types, and the distributed computing user experience.  

Science Conference Proceedings (OSTI)

This report summarizes the methodology and results of a user perspectives study conducted by the Community Driven Improvement of Globus Software (CDIGS) project. The purpose of the study was to document the work-related goals and challenges facing today's scientific technology users, to record their perspectives on Globus software and the distributed-computing ecosystem, and to provide recommendations to the Globus community based on the observations. Globus is a set of open source software components intended to provide a framework for collaborative computational science activities. Rather than attempting to characterize all users or potential users of Globus software, our strategy has been to speak in detail with a small group of individuals in the scientific community whose work appears to be the kind that could benefit from Globus software, learn as much as possible about their work goals and the challenges they face, and describe what we found. The result is a set of statements about specific individuals experiences. We do not claim that these are representative of a potential user community, but we do claim to have found commonalities and differences among the interviewees that may be reflected in the user community as a whole. We present these as a series of hypotheses that can be tested by subsequent studies, and we offer recommendations to Globus developers based on the assumption that these hypotheses are representative. Specifically, we conducted interviews with thirty technology users in the scientific community. We included both people who have used Globus software and those who have not. We made a point of including individuals who represent a variety of roles in scientific projects, for example, scientists, software developers, engineers, and infrastructure providers. The following material is included in this report: (1) A summary of the reported work-related goals, significant issues, and points of satisfaction with the use of Globus software; (2) A method for characterizing users according to their technology interactions, and identification of four user types among the interviewees using the method; (3) Four profiles that highlight points of commonality and diversity in each user type; (4) Recommendations for technology developers and future studies; (5) A description of the interview protocol and overall study methodology; (6) An anonymized list of the interviewees; and (7) Interview writeups and summary data. The interview summaries in Section 3 and transcripts in Appendix D illustrate the value of distributed computing software--and Globus in particular--to scientific enterprises. They also document opportunities to make these tools still more useful both to current users and to new communities. We aim our recommendations at developers who intend their software to be used and reused in many applications. (This kind of software is often referred to as 'middleware.') Our two core recommendations are as follows. First, it is essential for middleware developers to understand and explicitly manage the multiple user products in which their software components are used. We must avoid making assumptions about the commonality of these products and, instead, study and account for their diversity. Second, middleware developers should engage in different ways with different kinds of users. Having identified four general user types in Section 4, we provide specific ideas for how to engage them in Section 5.

Childers, L.; Liming, L.; Foster, I.; Mathematics and Computer Science; Univ. of Chicago

2008-10-15T23:59:59.000Z

371

Malczewski Product Design LLC | Open Energy Information  

Open Energy Info (EERE)

Malczewski Product Design LLC Malczewski Product Design LLC Jump to: navigation, search Name Malczewski Product Design LLC Place Neenah, Wisconsin Zip 54956 Sector Wind energy Product Product development start-up planning to design, develop, patent, and distribute a new type of wind generator. Coordinates 44.186095°, -88.461954° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":44.186095,"lon":-88.461954,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

372

Inclusive Production of the X(3872)  

E-Print Network (OSTI)

If the X(3872) is a loosely-bound D^{*0} Dbar^0 / D^0 Dbar^{*0} molecule, its inclusive production rate can be described by the NRQCD factorization formalism that applies to inclusive quarkonium production. We argue that if the molecule has quantum numbers J^{PC} = 1^{++}, the most important term in the factorization formula should be the color-octet ^3S_1 term. This is also one of the two most important terms in the factorization formulas for chi_{cJ}. Since the color-octet ^3S_1 term dominates chi_{cJ} production for many processes, the ratio of the inclusive direct production rates for X and chi_{cJ} should be roughly the same for these processes. The assumption that the ratio of the production rates for X and chi_{cJ} is the same for all processes is used to estimate the inclusive production rate of X in B meson decays, Z^0 decays, and in p pbar collisions.

Eric Braaten

2004-08-20T23:59:59.000Z

373

Hydrogen Production Cost Estimate Using Biomass Gasification: Independent Review  

DOE Green Energy (OSTI)

This independent review is the conclusion arrived at from data collection, document reviews, interviews and deliberation from December 2010 through April 2011 and the technical potential of Hydrogen Production Cost Estimate Using Biomass Gasification. The Panel reviewed the current H2A case (Version 2.12, Case 01D) for hydrogen production via biomass gasification and identified four principal components of hydrogen levelized cost: CapEx; feedstock costs; project financing structure; efficiency/hydrogen yield. The panel reexamined the assumptions around these components and arrived at new estimates and approaches that better reflect the current technology and business environments.

Ruth, M.

2011-10-01T23:59:59.000Z

374

The economics of biomass production in the United States  

DOE Green Energy (OSTI)

Biomass crops (e.g. poplar, willow, switchgrass) could become important feedstocks for power, liquid fuel, and chemical production. This paper presents estimates of the potential production of biomass in the US under a range of assumptions. Estimates of potential biomass crop yields and production costs from the Department of Energy`s (DOE) Oak Ridge National Laboratories (ORNL) are combined with measures of land rents from USDA`s Conservation Reserve Program (CRP), to estimate a competitive supply of biomass wood and grass crops. Estimates are made for one potential biomass use--electric power production--where future costs of electricity production from competing fossil fuels set the demand price. The paper outlines the methodology used and limitations of the analysis.

Graham, R.L.; Walsh, M.E. [Oak Ridge National Lab., TN (United States); Lichtenberg, E. [Univ. of Maryland, College Park, MD (United States); Roningen, V.O. [ERS-USDA, Washington, DC (United States); Shapouri, H. [OENU-ERS-USDA, Washington, DC (United States)

1995-12-31T23:59:59.000Z

375

EV Solar Products | Open Energy Information  

Open Energy Info (EERE)

Solar Products Solar Products Jump to: navigation, search Logo: EV Solar Products Name EV Solar Products Address 2655 N. Highway 89 Place Chino Valley, Arizona Zip 86323 Sector Solar Product renewable energy products and services Year founded 1991 Phone number (928) 636-2201 Website http://www.evsolar.com/ Coordinates 34.8387989°, -112.4600036° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":34.8387989,"lon":-112.4600036,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

376

Alternative Fuels Data Center: Ethanol Production Incentive  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Production Production Incentive to someone by E-mail Share Alternative Fuels Data Center: Ethanol Production Incentive on Facebook Tweet about Alternative Fuels Data Center: Ethanol Production Incentive on Twitter Bookmark Alternative Fuels Data Center: Ethanol Production Incentive on Google Bookmark Alternative Fuels Data Center: Ethanol Production Incentive on Delicious Rank Alternative Fuels Data Center: Ethanol Production Incentive on Digg Find More places to share Alternative Fuels Data Center: Ethanol Production Incentive on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Ethanol Production Incentive Montana-based ethanol producers are eligible for a tax incentive of $0.20 per gallon of ethanol produced solely from Montana agricultural products or

377

Alternative Fuels Data Center: Biodiesel Production Incentive  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Biodiesel Production Biodiesel Production Incentive to someone by E-mail Share Alternative Fuels Data Center: Biodiesel Production Incentive on Facebook Tweet about Alternative Fuels Data Center: Biodiesel Production Incentive on Twitter Bookmark Alternative Fuels Data Center: Biodiesel Production Incentive on Google Bookmark Alternative Fuels Data Center: Biodiesel Production Incentive on Delicious Rank Alternative Fuels Data Center: Biodiesel Production Incentive on Digg Find More places to share Alternative Fuels Data Center: Biodiesel Production Incentive on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Biodiesel Production Incentive A qualified Kansas biodiesel producer is eligible for a production incentive of $0.30 per gallon of biodiesel sold. The incentive is payable

378

Alternative Fuels Data Center: Biofuels Production Grants  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Biofuels Production Biofuels Production Grants to someone by E-mail Share Alternative Fuels Data Center: Biofuels Production Grants on Facebook Tweet about Alternative Fuels Data Center: Biofuels Production Grants on Twitter Bookmark Alternative Fuels Data Center: Biofuels Production Grants on Google Bookmark Alternative Fuels Data Center: Biofuels Production Grants on Delicious Rank Alternative Fuels Data Center: Biofuels Production Grants on Digg Find More places to share Alternative Fuels Data Center: Biofuels Production Grants on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Biofuels Production Grants The Biofuels Production Incentive Grant Program provides grants to producers of advanced biofuels, specifically fuels derived from any

379

Alternative Fuels Data Center: Ethanol Production Incentive  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Ethanol Production Ethanol Production Incentive to someone by E-mail Share Alternative Fuels Data Center: Ethanol Production Incentive on Facebook Tweet about Alternative Fuels Data Center: Ethanol Production Incentive on Twitter Bookmark Alternative Fuels Data Center: Ethanol Production Incentive on Google Bookmark Alternative Fuels Data Center: Ethanol Production Incentive on Delicious Rank Alternative Fuels Data Center: Ethanol Production Incentive on Digg Find More places to share Alternative Fuels Data Center: Ethanol Production Incentive on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Ethanol Production Incentive Ethanol producers may qualify for an income tax credit equal to 30% of production facility nameplate capacity between 500,000 and 15 million

380

Alternative Fuels Data Center: Biofuels Production Incentive  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Biofuels Production Biofuels Production Incentive to someone by E-mail Share Alternative Fuels Data Center: Biofuels Production Incentive on Facebook Tweet about Alternative Fuels Data Center: Biofuels Production Incentive on Twitter Bookmark Alternative Fuels Data Center: Biofuels Production Incentive on Google Bookmark Alternative Fuels Data Center: Biofuels Production Incentive on Delicious Rank Alternative Fuels Data Center: Biofuels Production Incentive on Digg Find More places to share Alternative Fuels Data Center: Biofuels Production Incentive on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Biofuels Production Incentive Qualified ethanol and biodiesel producers are eligible for production incentives on a per gallon basis. To be eligible for the incentive, the

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


381

Alternative Fuels Data Center: Ethanol Production Incentive  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Ethanol Production Ethanol Production Incentive to someone by E-mail Share Alternative Fuels Data Center: Ethanol Production Incentive on Facebook Tweet about Alternative Fuels Data Center: Ethanol Production Incentive on Twitter Bookmark Alternative Fuels Data Center: Ethanol Production Incentive on Google Bookmark Alternative Fuels Data Center: Ethanol Production Incentive on Delicious Rank Alternative Fuels Data Center: Ethanol Production Incentive on Digg Find More places to share Alternative Fuels Data Center: Ethanol Production Incentive on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Ethanol Production Incentive Qualified ethanol producers are eligible for a production incentive payable from the Kansas Qualified Agricultural Ethyl Alcohol Producer Fund. An

382

Discriminative sum types locate the source of type errors  

Science Conference Proceedings (OSTI)

We propose a type system for locating the source of type errors in an applied lambda calculus with ML-style polymorphism. The system is based on discriminative sum types---known from work on soft typing---with annotation subtyping and recursive types. ... Keywords: polymorphism, type errors, type inference

Matthias Neubauer; Peter Thiemann

2003-08-01T23:59:59.000Z

383

Production Practice  

Science Conference Proceedings (OSTI)

...Figure 1 shows the sequence of shapes in the production of a hollow handle for a table knife formed and coined in a 410 kg (900 lb) pneumatic drop hammer. The work metal was 0.81 mm (0.032 in.) thick copper alloy C75700 (nickel silver, 65â??12) annealed to a hardness of 35 to 45 HRB; blank size was 25 by...

384

Angular distribution and azimuthal asymmetry for pentaquark production in proton-proton collisions  

E-Print Network (OSTI)

Angular distributions for production of the $\\Theta^+$ pentaquark are calculated for the collisions of polarized protons with polarized target protons. We compare calculations based on different assumptions concerning spin and parity ($J=1/2^\\pm,3/2^\\pm$) of the $\\Theta^+$ state. For a wide class of interactions the spin correlation parameters describing the asymmetric angular distributions are calculated up to 250 MeV above production threshold. The deviations from the near threshold behavior are investigated.

H. W. Barz; M. Zetenyi

2004-11-01T23:59:59.000Z

385

Biofuel Production  

E-Print Network (OSTI)

Copyright © 2011 Hiroshi Sakuragi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Large amounts of fossil fuels are consumed every day in spite of increasing environmental problems. To preserve the environment and construct a sustainable society, the use of biofuels derived from different kinds of biomass is being practiced worldwide. Although bioethanol has been largely produced, it commonly requires food crops such as corn and sugar cane as substrates. To develop a sustainable energy supply, cellulosic biomass should be used for bioethanol production instead of grain biomass. For this purpose, cell surface engineering technology is a very promising method. In biobutanol and biodiesel production, engineered host fermentation has attracted much attention; however, this method has many limitations such as low productivity and low solvent tolerance of microorganisms. Despite these problems, biofuels such as bioethanol, biobutanol, and biodiesel are potential energy sources that can help establish a sustainable society. 1.

Hiroshi Sakuragi; Kouichi Kuroda; Mitsuyoshi Ueda

2010-01-01T23:59:59.000Z

386

Product Efficiency Cases  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

product-efficiency-cases Office of Hearings and product-efficiency-cases Office of Hearings and Appeals 1000 Independence Ave., SW Washington, DC, 20585 202-287-1566 en EXC-13-0004 - In the Matter of Liebherr Canada Ltd. http://energy.gov/oha/downloads/exc-13-0004-matter-liebherr-canada-ltd EXC-13-0004 - In the Matter of Liebherr Canada Ltd.

387

An Improved Type Reduction Algorithm for Type-2 Fuzzy Sets.  

E-Print Network (OSTI)

??Type reduction does the work of computing the centroid of a type-2 fuzzy set. The result is a type-1 fuzzy set from which a corresponding… (more)

Su, Yao-Lung

2011-01-01T23:59:59.000Z

388

Screw Type Ac Air Compressor Manufacturers, Screw Type Ac Air ...  

U.S. Energy Information Administration (EIA)

Screw Type Ac Air Compressor, Screw Type Ac Air Compressor Manufacturers & Suppliers Directory - Find here Screw Type Ac Air Compressor Traders, ...

389

EERE Publication and Product Library  

NLE Websites -- All DOE Office Websites (Extended Search)

Browse By Topic Browse By Topic Browse the Products and Publications by expanding/selecting from the topic tree below. Make selection(s) in the 'Information For' box to filter your results by audience type. Similarly, use the 'Information Type' box to filter your results by the type of information presented. Topics Bioenergy Technologies Office Building Technologies Office Federal Energy Management Program Geothermal Technologies Office Fuel Cell Technologies Office Advanced Manufacturing Office Solar Energy Technologies Program Vehicle Technologies Office Wind Program Weatherization & Intergovernmental Program General Topics Energy Savers Water Power Program Energy Analysis State and Local Energy Efficiency Action Network (SEE Action) Office of EERE Select Clear Topic Skip Navigation Links.

390

MEMORANDUM TO: FILE FROM: TYPE OF OPERATION  

Office of Legacy Management (LM)

, , TYPE OF OPERATION ~_--_-----_---___ 69 Research & Development a Facility. Type 0 Production scale testing Cl Pilat Scale IK Bench Scale Process 0 Theoretical Studies u Sample & Analysis q Production 0 Disposal/Storage a Manufacturing 0 University 0 Research Organization 0 Government Sponsored Facility 0 Other --------------__----- TYPE OF CONTRACT ---------------- 0 Prime 0 Other information (i.e., cost 0 Subcontractor + fixed fee, unit price, 5 Purchase Order ~SlvtM ay LuPo~l- time & material, r+c) _L-G~-~~~ ------GA------ Contract/Purchase Order # 3 I -? ciYl---------------------------- AZ. FG CONTRACTING PERIOD: --------__--_____- ----&-b&-f zw------ ______________ OWNERSHIP: AEC/MED AEC/MED OWE_3 LE_A_sEQ GOUT GOVT CONTRACTOR CONTRACTOR

391

Assumptions to Annual Energy Outlook - Energy Information ...  

U.S. Energy Information Administration (EIA)

Analysis & Projections. Monthly and yearly energy forecasts, analysis of energy topics, financial analysis, Congressional reports. Markets & ...

392

Assumptions to the Annual Energy Outlook 2006  

Gasoline and Diesel Fuel Update (EIA)

6) 6) Release date: March 2006 Next release date: March 2007 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 International Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Commercial Demand Module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Natural Gas Transmission and Distribution Module. . . . . . . . . . . . . . . . . . . . . . 101 Petroleum Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Coal Market Module

393

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Household Expenditures Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Commercial Demand Module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Natural Gas Transmission and Distribution Module . . . . . . . . . . . . . . . . . . . . . . 99 Petroleum Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Coal Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Renewable Fuels Module . . . . . . . . . . .

394

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas supply. A detailed description of the OGSM is provided in the EIA publication, Model Documentation Report: The Oil and Gas Supply Module (OGSM), DOE/EIA-M063(2003), (Washington, DC, February 2003). The OGSM provides crude oil and natural gas short-term supply parameters to both the Natural Gas Transmission and Distribution Module and the Petroleum Market Module. The OGSM simulates the activity of numerous firms that produce oil and natural gas from domestic fields throughout the United States, acquire natural gas from foreign producers for resale in the United States, or sell U.S. gas to foreign consumers.

395

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Energy Module Oil and Gas Supply Module Household Expenditures Module Natural Gas Transmission and Distribution Module Residential Demand Module Petroleum Market Module...

396

Operational Waste Stream Assumption for TSLCC Estimates  

Science Conference Proceedings (OSTI)

This document provides the background and basis for the operational waste stream used in the 2000 Total System Life Cycle Cost (TSLCC) estimate for the Civilian Radioactive Waste Management System (CRWMS). This document has been developed in accordance with its Development Plan (CRWMS M&O 2000a), and AP-3.11Q, ''Technical Reports''.

S. Gillespie

2000-09-01T23:59:59.000Z

397

Green enterprise computing data: Assumptions and realities  

Science Conference Proceedings (OSTI)

Until now, green computing research has largely relied on few, short-term power measurements to characterize the energy use of enterprise computing. This paper brings new and comprehensive power datasets through Powernet, a hybrid sensor network that ...

Maria Kazandjieva; Brandon Heller; Omprakash Gnawali; Philip Levis; Christos Kozyrakis

2012-06-01T23:59:59.000Z

398

Testing model assumptions in functional regression models  

Science Conference Proceedings (OSTI)

In the functional regression model where the responses are curves, new tests for the functional form of the regression and the variance function are proposed, which are based on a stochastic process estimating L^2-distances. Our approach avoids the explicit ... Keywords: 62G10, Functional data, Goodness-of-fit tests, Parametric bootstrap, Tests for heteroscedasticity

Axel Bücher; Holger Dette; Gabriele Wieczorek

2011-11-01T23:59:59.000Z

399

Supply-side Resources & Planning Assumptions  

E-Print Network (OSTI)

modeling 146/19/2013 #12;6/19/2013 8 Commercial w/Limited PNW availability Proposed resources: ­ Biogas technologies Raft River Geothermal (ID)Biogas technologies Landfill Wastewater treatment Animal, commercial

400

Assumptions to the Annual Energy Outlook  

Annual Energy Outlook 2012 (EIA)

eleven building categories14 in each of the nine Census divisions. The model begins by developing forecasts of floorspace for the 99 building category and Census division...

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


401

Current Status and Future Assumptions INTRODUCTION  

E-Print Network (OSTI)

supplies (coal bed methane, tight sands, oil shale), increased import capability through May 2005 2-9 #12

402

Assumptions to Annual Energy Outlook - Energy Information ...  

U.S. Energy Information Administration (EIA)

Energy Information Administration - EIA ... Financial market analysis and financial data for major energy companies. Environment. Greenhouse gas data, ...

403

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

2003 PDF files require Adobe Acrobat Reader, the latest copy of which is available free Adobe Acrobat Logo Appendixes Available Formats Download Entire Report PDF, HTML...

404

Assumptions to the Annual Energy Outlook 2003  

Gasoline and Diesel Fuel Update (EIA)

to 26.57 per barrel in the reference case Fully integrated High World Oil Price World oil prices are 33.05 per barrel in 2025,compared to 26.57 per barrel in the reference...

405

Assumptions to the Annual Energy Outlook 2009  

U.S. Energy Information Administration (EIA)

(EIEA2008), the biofuel provisions of the Food, ... and raw materials in each of 21 industries, subject to the delivered prices of energy and macroeconomic

406

Synthetic fuels: production and products  

DOE Green Energy (OSTI)

A brief primer on synthetic fuels is given. The paper includes brief descriptions of generic conversion technologies that can be used to convert various raw materials such as coal, oil shale, tar sands, peat, and biomass into synthetic fuels similar in character to petroleum-derived fuels currently in commerce. References for additional information on synthetic fuel processes and products are also given in the paper.

Singh, S.P.N.

1984-01-01T23:59:59.000Z

407

Synthetic fuels: production and products  

DOE Green Energy (OSTI)

A brief review on synthetic fuels is given. The paper includes brief descriptions of generic conversion technologies that can be used to convert various raw materials such as coal, oil shale, tar sands, peat and biomass into synthetic fuels similar in character to petroleum-derived fuels currently in commerce. Because the subject is vast and the space is limited, references for additional information on synthetic fuel processes and products are also given in the paper. 24 references.

Singh, S.P.

1985-08-01T23:59:59.000Z

408

Pityriasis rubra pilaris, type IV  

E-Print Network (OSTI)

Pityriasis rubra pilaris, type IV Jennifer Bragg MD,rubra pilaris (PRP), type IV (circumscribed juvenile).Type IV PRP develops in prepubertal children, is typically

Bragg, Jennifer; Witkiewicz, Agnieszka; Orlow, Seth J; Schaffer, Julie V

2005-01-01T23:59:59.000Z

409

Pityriasis rubra pilaris, type 1  

E-Print Network (OSTI)

Pityriasis rubra pilaris, type 1 Alexandria V Booth MD andhemorrhages [ 1 ]. Five types of pityriasis rubra pilarisand prognosis. The five types include the classic adult and

Booth, Alexandria V; Ma, Linglei

2005-01-01T23:59:59.000Z

410

Production I/O Characterization on the Cray XE6 | Argonne National...  

NLE Websites -- All DOE Office Websites (Extended Search)

Production IO Characterization on the Cray XE6 Title Production IO Characterization on the Cray XE6 Publication Type Conference Paper Year of Publication 2013 Authors Carns, PH,...

411

Sugar Production  

NLE Websites -- All DOE Office Websites (Extended Search)

Sugar Production Sugar Production Name: Lauren Location: N/A Country: N/A Date: N/A Question: This is the experiment I did: our class took 6 sugars, placed them in test tubes and put three drops of yeast in each test tube. we then placed them in the incubator for one day and the next day looked at our results. the purpose was to find out with sugar would produce the most carbon dioxide. two of the sugars that we tested were LACTOSE and STARCH. my question is, why are lactose and starch the only sugars who didn't produce any, or very very little, carbon dioxide? and how is this process related to glycolysis? Replies: Bacteria and yeast are very efficient with their enzyme systems. They don't make enzymes they can't use. Yeast don't have the enzymes necessary to metabolize lactose. Starch is a complex sugar and yeast needs certain enzymes to break starch down into sugar. Every chemical reaction needs its own enzyme.

412

Types of Thermocouples  

Science Conference Proceedings (OSTI)

Table 1   Properties of standard thermocouples...Table 1 Properties of standard thermocouples Type Thermoelements Base composition Melting point, °C Resisivity nΩ · m Recommended

413

Manufacturer: Panasonic Battery Type: ...  

NLE Websites -- All DOE Office Websites (Extended Search)

Battery Specifi cations Manufacturer: Panasonic Battery Type: Nickel Metal Hydride Rated Capacity: 5.5 Ahr Rated Power: Not Available Nominal Pack Voltage: 158.4 VDC Nominal Cell...

414

Type I Tanks  

NLE Websites -- All DOE Office Websites (Extended Search)

I Tanks I Tanks * 12 Type I tanks were built between 1951-53 * 750,000 gallon capacity; 75 feet in diameter by 24 ½ feet high * Partial secondary containment with leak detection * Contain approximately 10 percent of the waste volume * 7 Type I tanks have leaked waste into the tank annulus; the amount of waste stored in these tanks is kept below the known leak sites that have appeared over the decades of

415

Alternative Fuels Data Center: Biofuels Production Promotion  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Biofuels Production Biofuels Production Promotion to someone by E-mail Share Alternative Fuels Data Center: Biofuels Production Promotion on Facebook Tweet about Alternative Fuels Data Center: Biofuels Production Promotion on Twitter Bookmark Alternative Fuels Data Center: Biofuels Production Promotion on Google Bookmark Alternative Fuels Data Center: Biofuels Production Promotion on Delicious Rank Alternative Fuels Data Center: Biofuels Production Promotion on Digg Find More places to share Alternative Fuels Data Center: Biofuels Production Promotion on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Biofuels Production Promotion The state legislature supports the Federal "25 x 25" initiative, under which 25% of the total energy consumed in the United States by 2025 would

416

Alternative Fuels Data Center: Ethanol Production Credit  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Ethanol Production Ethanol Production Credit to someone by E-mail Share Alternative Fuels Data Center: Ethanol Production Credit on Facebook Tweet about Alternative Fuels Data Center: Ethanol Production Credit on Twitter Bookmark Alternative Fuels Data Center: Ethanol Production Credit on Google Bookmark Alternative Fuels Data Center: Ethanol Production Credit on Delicious Rank Alternative Fuels Data Center: Ethanol Production Credit on Digg Find More places to share Alternative Fuels Data Center: Ethanol Production Credit on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Ethanol Production Credit County governments are eligible to receive waste reduction credits for using yard clippings, clean wood waste, or paper waste as feedstock for the

417

Alternative Fuels Data Center: Biodiesel Production Tax  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Biodiesel Production Biodiesel Production Tax to someone by E-mail Share Alternative Fuels Data Center: Biodiesel Production Tax on Facebook Tweet about Alternative Fuels Data Center: Biodiesel Production Tax on Twitter Bookmark Alternative Fuels Data Center: Biodiesel Production Tax on Google Bookmark Alternative Fuels Data Center: Biodiesel Production Tax on Delicious Rank Alternative Fuels Data Center: Biodiesel Production Tax on Digg Find More places to share Alternative Fuels Data Center: Biodiesel Production Tax on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Biodiesel Production Tax A private biodiesel producer that produces less than 5,000 gallons of biodiesel annually is subject to the annual state motor fuel tax. The

418

Alternative Fuels Data Center: Ethanol Production Incentive  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Ethanol Production Ethanol Production Incentive to someone by E-mail Share Alternative Fuels Data Center: Ethanol Production Incentive on Facebook Tweet about Alternative Fuels Data Center: Ethanol Production Incentive on Twitter Bookmark Alternative Fuels Data Center: Ethanol Production Incentive on Google Bookmark Alternative Fuels Data Center: Ethanol Production Incentive on Delicious Rank Alternative Fuels Data Center: Ethanol Production Incentive on Digg Find More places to share Alternative Fuels Data Center: Ethanol Production Incentive on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Ethanol Production Incentive The Missouri Department of Agriculture manages the Missouri Ethanol Producer Incentive Fund (Fund), which provides monthly grants to qualified

419

DEVELOPMENT OF A PRODUCTION COST ESTIMATION FRAMEWORK TO SUPPORT PRODUCT FAMILY DESIGN  

E-Print Network (OSTI)

The main task of a product family designer is to decide the right components/design variables to share among products to maintain economies of scale with minimum sacrifice in the performance of each product in the family. The decisions are usually based on several criteria, but production cost is of primary concern. Estimating the production cost of a family of products involves both estimating the production cost of each product in the family and the costs incurred by common and variant components/design variables in the family. To estimate these costs consistently and accurately, we propose a production cost estimation framework to support product family design based on Activity-Based Costing (ABC) that consists of three stages: (1) allocation, (2) estimation, and (3) analysis. In the allocation stage, the production activities and resources needed to produce the entire products in a family are identified and classified with an activity table, a resource table, and a production flow. To help allocate product data for production, a product family structure is represented by a hierarchical classification of products that form the product family. In the estimation stage, production costs are estimated with cost estimation methods selected based on the type of information available. In the analysis stage,

Jaeil Park; Timothy W. Simpson

2004-01-01T23:59:59.000Z

420

Production Services  

NLE Websites -- All DOE Office Websites (Extended Search)

Welcome Welcome The Production Services site contains links to each of the division's groups with descriptions of their services. Our goal is to update this website frequently to reflect ongoing service upgrades which, by planning and design, are added so that we can continue to meet your needs in a constantly changing work environment. Note: The Graphic Design Studio has been relocated to the second floor in the north wing of the Research Support Building 400. The telephone number remains the same, X7288. If you have any questions, please call supervisor, Rick Backofen, X6183. Photography Photography services are available at no charge to BNL and Guest users. See a list of the complete range of photography services available. Video Video services are available at no charge to BNL and Guest users. See a list of the complete range of video services available.

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


421

Positive Energy Conservation Products | Open Energy Information  

Open Energy Info (EERE)

Positive Energy Conservation Products Positive Energy Conservation Products Jump to: navigation, search Name Positive Energy Conservation Products Address PO Box 7568 Place Boulder, Colorado Zip 80306 Sector Efficiency Product Distributor of energy efficiency products - lighting, heating, cooling, water Website http://www.positive-energy.com Coordinates 40.0153°, -105.2702° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":40.0153,"lon":-105.2702,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

422

Heartland Corn Products | Open Energy Information  

Open Energy Info (EERE)

Corn Products Corn Products Jump to: navigation, search Name Heartland Corn Products Place Winthrop, Minnesota Zip 55396 Product Heartland Corn Products is farmer-owned cooperative that produces corn-derived ethanol. Coordinates 48.47373°, -120.177559° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":48.47373,"lon":-120.177559,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

423

Ashworths Products Ltd | Open Energy Information  

Open Energy Info (EERE)

Ashworths Products Ltd Ashworths Products Ltd Jump to: navigation, search Name Ashworths Products Ltd Place Lancashire, England, United Kingdom Product Lancashire-based producer of biodiesel from waste animal and vegetable fats, most of which is used for energy production through co-firing. Coordinates 53.86121°, -2.56483° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":53.86121,"lon":-2.56483,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

424

Alternative Fuels Data Center: Ethanol Production Equipment Tax Exemption  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Ethanol Production Ethanol Production Equipment Tax Exemption to someone by E-mail Share Alternative Fuels Data Center: Ethanol Production Equipment Tax Exemption on Facebook Tweet about Alternative Fuels Data Center: Ethanol Production Equipment Tax Exemption on Twitter Bookmark Alternative Fuels Data Center: Ethanol Production Equipment Tax Exemption on Google Bookmark Alternative Fuels Data Center: Ethanol Production Equipment Tax Exemption on Delicious Rank Alternative Fuels Data Center: Ethanol Production Equipment Tax Exemption on Digg Find More places to share Alternative Fuels Data Center: Ethanol Production Equipment Tax Exemption on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Ethanol Production Equipment Tax Exemption

425

Alternative Fuels Data Center: Biofuels Production Facility Grants  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Biofuels Production Biofuels Production Facility Grants to someone by E-mail Share Alternative Fuels Data Center: Biofuels Production Facility Grants on Facebook Tweet about Alternative Fuels Data Center: Biofuels Production Facility Grants on Twitter Bookmark Alternative Fuels Data Center: Biofuels Production Facility Grants on Google Bookmark Alternative Fuels Data Center: Biofuels Production Facility Grants on Delicious Rank Alternative Fuels Data Center: Biofuels Production Facility Grants on Digg Find More places to share Alternative Fuels Data Center: Biofuels Production Facility Grants on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Biofuels Production Facility Grants The Renewable Fuels Development Program provides grants for the

426

Alternative Fuels Data Center: Biodiesel Production Investment Tax Credit  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Biodiesel Production Biodiesel Production Investment Tax Credit to someone by E-mail Share Alternative Fuels Data Center: Biodiesel Production Investment Tax Credit on Facebook Tweet about Alternative Fuels Data Center: Biodiesel Production Investment Tax Credit on Twitter Bookmark Alternative Fuels Data Center: Biodiesel Production Investment Tax Credit on Google Bookmark Alternative Fuels Data Center: Biodiesel Production Investment Tax Credit on Delicious Rank Alternative Fuels Data Center: Biodiesel Production Investment Tax Credit on Digg Find More places to share Alternative Fuels Data Center: Biodiesel Production Investment Tax Credit on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Biodiesel Production Investment Tax Credit

427

Alternative Fuels Data Center: Hydrogen Production and Retail Requirements  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Hydrogen Production Hydrogen Production and Retail Requirements to someone by E-mail Share Alternative Fuels Data Center: Hydrogen Production and Retail Requirements on Facebook Tweet about Alternative Fuels Data Center: Hydrogen Production and Retail Requirements on Twitter Bookmark Alternative Fuels Data Center: Hydrogen Production and Retail Requirements on Google Bookmark Alternative Fuels Data Center: Hydrogen Production and Retail Requirements on Delicious Rank Alternative Fuels Data Center: Hydrogen Production and Retail Requirements on Digg Find More places to share Alternative Fuels Data Center: Hydrogen Production and Retail Requirements on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Hydrogen Production and Retail Requirements

428

Alternative Fuels Data Center: Biofuels Production Property Tax Exemption  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Biofuels Production Biofuels Production Property Tax Exemption to someone by E-mail Share Alternative Fuels Data Center: Biofuels Production Property Tax Exemption on Facebook Tweet about Alternative Fuels Data Center: Biofuels Production Property Tax Exemption on Twitter Bookmark Alternative Fuels Data Center: Biofuels Production Property Tax Exemption on Google Bookmark Alternative Fuels Data Center: Biofuels Production Property Tax Exemption on Delicious Rank Alternative Fuels Data Center: Biofuels Production Property Tax Exemption on Digg Find More places to share Alternative Fuels Data Center: Biofuels Production Property Tax Exemption on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Biofuels Production Property Tax Exemption

429

Alternative Fuels Data Center: Ethanol Production Investment Tax Credits  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Ethanol Production Ethanol Production Investment Tax Credits to someone by E-mail Share Alternative Fuels Data Center: Ethanol Production Investment Tax Credits on Facebook Tweet about Alternative Fuels Data Center: Ethanol Production Investment Tax Credits on Twitter Bookmark Alternative Fuels Data Center: Ethanol Production Investment Tax Credits on Google Bookmark Alternative Fuels Data Center: Ethanol Production Investment Tax Credits on Delicious Rank Alternative Fuels Data Center: Ethanol Production Investment Tax Credits on Digg Find More places to share Alternative Fuels Data Center: Ethanol Production Investment Tax Credits on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Ethanol Production Investment Tax Credits

430

A Study of the Energy Efficiency of Hadronic Reactors of Molecular Type  

E-Print Network (OSTI)

In this paper, we introduce an estimate of the "commercial efficiency" of Santilli's hadronic reactors of molecular type (Patented and International Patents Pending) which convert a liquid feedstock (such as automotive antifreeze and oil waste, city or farm liquid waste, crude oil, etc.) into the clean burning magnegas plus heat acquired by the liquid feedstock. The "commercial efficiency" is defined as the ratio between the total energy output (energy in magnegas plus heat) and the electric energy used for its production, while the "scientific efficiency" is the usual ratio between the total energy output and the total energy input (the sum of the electric energy plus the energy in the liquid feedstock as well as that in the carbon electrodes). A primary purpose of this paper is to show that conventional thermochemistry does indeed predict a commercial efficiency bigger than one, although their values is considerably smaller than the actual efficiency measured in the reactors, thus indicating the applicability of the covering hadronic chemistry from which the reactors have received their name. We reach an upper limit of the commercial efficiency of 3.11 for the use of pure water as feedstock, and of 3.11 to 7.5 for a mixture of ethyleneglicole and water. The study of the heat produced by the reactions leads to large divergencies between the thermochemical predictions and experimental data of at least a factor of three. Such divergencies can only be explained with deviations from quantum chemistry in favor of the covering hadronic chemistry. In particular, the indicated large divergencies can only be explained with the assumption that the produced combustible gas has the new non-valence chemical structure of Santilli magnecules.

A. K. Aringazin; R. M. Santilli

2001-12-20T23:59:59.000Z

431

Figure 2. Stratigraphic Summary of Ages, Names and Rock Types...  

U.S. Energy Information Administration (EIA) Indexed Site

2. Stratigraphic Summary of Ages, Names and Rock Types in the ANWR 1002 and Coastal Plain Area of the Alaska North Slope. Potentially Productive Reservoirs and Plays Assessed by...

432

The Production of High-Quality Magnesite Ore Concentrate With ...  

Science Conference Proceedings (OSTI)

Thus, high-quality magnesite ore with permroll type magnetic separators, were produced. ... Environmental Assessment of Li-CNT Battery Production.

433

MARINE BIOMASS SYSTEM: ANAEROBIC DIGESTION AND PRODUCTION OF METHANE  

E-Print Network (OSTI)

Design Parameters Marine Biomass Production Sea Farmof Various Types of Biomass . Biomethanation Parameters.Proceedings, Fuels from Biomass Symposium. University of

Haven, Kendall F.

2011-01-01T23:59:59.000Z

434

Insulation and Air Sealing Products and Services | Department...  

NLE Websites -- All DOE Office Websites (Extended Search)

applications of structural insulated panels Types of Fiberglass and Rock and Slag Wool Building Insulation Products North American Insulation Manufacturers Association...

435

The Napier Type System  

E-Print Network (OSTI)

Persistent programming is concerned with the construction of large and long lived systems of data. In designing and building persistent object systems, we are attempting to regularise the activities that are performed on data by programming languages, operating systems, database management systems and file systems. We have identified the following areas of research which we are investigating in the context of persistent systems. They are: controlling complexity, protection of data, orthogonal persistence, controlled system evolution and concurrent computation. In this paper, we describe the data modelling facilities of the Napier type system. We also demonstrate the flexible and incremental nature of the type checking mechanism that is required for persistent programming. The type system is central to the nature of the Napier language and we will demonstrate how it has been designed to solve problems in the five areas identified above.

R. Morrison; A.L. Brown; R. Carrick; R.C.H. Connor; A. Dearle; M.P. Atkinson

1989-01-01T23:59:59.000Z

436

Types of quantum information  

E-Print Network (OSTI)

Quantum, in contrast to classical, information theory, allows for different incompatible types (or species) of information which cannot be combined with each other. Distinguishing these incompatible types is useful in understanding the role of the two classical bits in teleportation (or one bit in one-bit teleportation), for discussing decoherence in information-theoretic terms, and for giving a proper definition, in quantum terms, of ``classical information.'' Various examples (some updating earlier work) are given of theorems which relate different incompatible kinds of information, and thus have no counterparts in classical information theory.

Robert B. Griffiths

2007-07-25T23:59:59.000Z

437

Conditional belief types  

E-Print Network (OSTI)

We study type spaces where a player’s type at a state is a conditional probability on the space. We axiomatize these type spaces using conditional belief operators, and examine three additional axioms of increasing strength. First, introspection, which requires the agent to be unconditionally certain of her beliefs. Second, echo, according to which the unconditional beliefs implied by the condition must be held given the condition. Third, determination, which says that the conditional beliefs are the unconditional beliefs that are conditionally certain. The echo axiom implies that conditioning on an event is the same as conditioning on the event being certain, which formalizes the standard informal interpretation of conditioning in probability theory. The echo axiom also implies that the conditional probability given an event is a prior of the unconditional probability. The game-theoretic application of our model, which we treat in the context of an example, sheds light on a number of basic issues in the analysis of extensive form games. Type spaces are closely related to the sphere models of counterfactual conditionals and to models of hypothetical knowledge, and we discuss these relationships in detail.

Alfredo Di; Tillio Joseph; Y. Halpern; Dov Samet

2013-01-01T23:59:59.000Z

438

Spray type wet scrubber  

SciTech Connect

A spray type wet scrubber includes a plurality of spray nozzles installed in parallel banks across the path of gas stream within the scrubber body, and partition walls held upright in grating fashion to divide the path of gas stream into a plurality of passages, each of which accommodates one of the spray nozzles.

Atsukawa, M.; Tatani, A.

1978-01-10T23:59:59.000Z

439

Types of Multinet System  

Science Conference Proceedings (OSTI)

A limiting factor in research on combining classifiers is a lack of awareness of the full range of available modular structures. One reason for this is that there is as yet little agreement on a means of describing and classifying types of multiple classifier ...

Amanda J. C. Sharkey

2002-06-01T23:59:59.000Z

440

Stone Tool Production  

E-Print Network (OSTI)

by the author. ) Stone Tool Production, Hikade, UEE 2010Short Citation: Hikade 2010, Stone Tool Production. UEE.Thomas, 2010, Stone Tool Production. In Willeke Wendrich (

Hikade, Thomas

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


441

FCT Hydrogen Production: Contacts  

NLE Websites -- All DOE Office Websites (Extended Search)

Contacts to someone by E-mail Share FCT Hydrogen Production: Contacts on Facebook Tweet about FCT Hydrogen Production: Contacts on Twitter Bookmark FCT Hydrogen Production:...

442

Pebble Bed Reactor Dust Production Model  

SciTech Connect

The operation of pebble bed reactors, including fuel circulation, can generate graphite dust, which in turn could be a concern for internal components; and to the near field in the remote event of a break in the coolant circuits. The design of the reactor system must, therefore, take the dust into account and the operation must include contingencies for dust removal and for mitigation of potential releases. Such planning requires a proper assessment of the dust inventory. This paper presents a predictive model of dust generation in an operating pebble bed with recirculating fuel. In this preliminary work the production model is based on the use of the assumption of proportionality between the dust production and the normal force and distance traveled. The model developed in this work uses the slip distances and the inter-pebble forces computed by the authors’ PEBBLES. The code, based on the discrete element method, simulates the relevant static and kinetic friction interactions between the pebbles as well as the recirculation of the pebbles through the reactor vessel. The interaction between pebbles and walls of the reactor vat is treated using the same approach. The amount of dust produced is proportional to the wear coefficient for adhesive wear (taken from literature) and to the slip volume, the product of the contact area and the slip distance. The paper will compare the predicted volume with the measured production rates. The simulation tallies the dust production based on the location of creation. Two peak production zones from intra pebble forces are predicted within the bed. The first zone is located near the pebble inlet chute due to the speed of the dropping pebbles. The second peak zone occurs lower in the reactor with increased pebble contact force due to the weight of supported pebbles. This paper presents the first use of a Discrete Element Method simulation of pebble bed dust production.

Abderrafi M. Ougouag; Joshua J. Cogliati

2008-09-01T23:59:59.000Z

443

Scripting the type inference process  

Science Conference Proceedings (OSTI)

To improve the quality of type error messages in functional programming languages,we propose four techniques which influence the behaviour of constraint-based type inference processes. These techniques take the form of externally supplied type inference ... Keywords: constraints, directives, domain-specific programming, type errors, type inference

Bastiaan Heeren; Jurriaan Hage; S. Doaitse Swierstra

2003-08-01T23:59:59.000Z

444

Alternative Fuels Data Center: Biofuels Production and Distribution  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Biofuels Production Biofuels Production and Distribution Contracts to someone by E-mail Share Alternative Fuels Data Center: Biofuels Production and Distribution Contracts on Facebook Tweet about Alternative Fuels Data Center: Biofuels Production and Distribution Contracts on Twitter Bookmark Alternative Fuels Data Center: Biofuels Production and Distribution Contracts on Google Bookmark Alternative Fuels Data Center: Biofuels Production and Distribution Contracts on Delicious Rank Alternative Fuels Data Center: Biofuels Production and Distribution Contracts on Digg Find More places to share Alternative Fuels Data Center: Biofuels Production and Distribution Contracts on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type

445

Alternative Fuels Data Center: Ethanol and Biobutanol Production Incentive  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Ethanol and Biobutanol Ethanol and Biobutanol Production Incentive to someone by E-mail Share Alternative Fuels Data Center: Ethanol and Biobutanol Production Incentive on Facebook Tweet about Alternative Fuels Data Center: Ethanol and Biobutanol Production Incentive on Twitter Bookmark Alternative Fuels Data Center: Ethanol and Biobutanol Production Incentive on Google Bookmark Alternative Fuels Data Center: Ethanol and Biobutanol Production Incentive on Delicious Rank Alternative Fuels Data Center: Ethanol and Biobutanol Production Incentive on Digg Find More places to share Alternative Fuels Data Center: Ethanol and Biobutanol Production Incentive on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Ethanol and Biobutanol Production Incentive

446

Alternative Fuels Data Center: Alternative Fuel Production Subsidy  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Fuel Fuel Production Subsidy Prohibition to someone by E-mail Share Alternative Fuels Data Center: Alternative Fuel Production Subsidy Prohibition on Facebook Tweet about Alternative Fuels Data Center: Alternative Fuel Production Subsidy Prohibition on Twitter Bookmark Alternative Fuels Data Center: Alternative Fuel Production Subsidy Prohibition on Google Bookmark Alternative Fuels Data Center: Alternative Fuel Production Subsidy Prohibition on Delicious Rank Alternative Fuels Data Center: Alternative Fuel Production Subsidy Prohibition on Digg Find More places to share Alternative Fuels Data Center: Alternative Fuel Production Subsidy Prohibition on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Alternative Fuel Production Subsidy Prohibition

447

Alternative Fuels Data Center: Sustainable Biofuels Production Practices  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Sustainable Biofuels Sustainable Biofuels Production Practices to someone by E-mail Share Alternative Fuels Data Center: Sustainable Biofuels Production Practices on Facebook Tweet about Alternative Fuels Data Center: Sustainable Biofuels Production Practices on Twitter Bookmark Alternative Fuels Data Center: Sustainable Biofuels Production Practices on Google Bookmark Alternative Fuels Data Center: Sustainable Biofuels Production Practices on Delicious Rank Alternative Fuels Data Center: Sustainable Biofuels Production Practices on Digg Find More places to share Alternative Fuels Data Center: Sustainable Biofuels Production Practices on AddThis.com... More in this section... Federal State Advanced Search All Laws & Incentives Sorted by Type Sustainable Biofuels Production Practices

448

Solar-type Variables  

E-Print Network (OSTI)

The rich acoustic oscillation spectrum in solar-type variables make these stars particularly interesting for studying fluid-dynamical aspects of the stellar interior. I present a summary of the properties of solar-like oscillations, how they are excited and damped and discuss some of the recent progress in using asteroseismic diagnostic techniques for analysing low-degree acoustic modes. Also the effects of stellar-cycle variations in low-mass main-sequence stars are addressed.

Houdek, Gunter

2009-01-01T23:59:59.000Z

449

Petroleum - Exploration & Production - EIA  

U.S. Energy Information Administration (EIA)

Exploration and reserves, storage, imports and exports, production, prices, sales. Electricity. ... Oil Production Capacity Expansion Costs for the Persian Gulf.

450

Pentanes Plus Pipeline Stocks by Type - U.S. Energy Information ...  

U.S. Energy Information Administration (EIA)

Stock Type: Download Series History: Definitions, Sources & Notes: Show Data By: Product: Stock Type: Area: 2007 2008 2009 2010 2011 2012 View History; U.S. 1,219 ...

451

List of Portfolio Manager property types, definitions, and use details |  

NLE Websites -- All DOE Office Websites (Extended Search)

List of Portfolio Manager property types, definitions, and use List of Portfolio Manager property types, definitions, and use details Secondary menu About us Press room Contact Us Portfolio Manager Login Facility owners and managers Existing buildings Commercial new construction Industrial energy management Small business Service providers Service and product providers Verify applications for ENERGY STAR certification Design commercial buildings Energy efficiency program administrators Commercial and industrial program sponsors Associations State and local governments Federal agencies Tools and resources Training In This Section Campaigns Commercial building design Communications resources Energy management guidance Financial resources Portfolio Manager Products and purchasing Recognition Research and reports Service and product provider (SPP) resources

452

Practical pluggable types for Java  

E-Print Network (OSTI)

This paper introduces the Checker Framework, which supports adding pluggable type systems to the Java language in a backward-compatible way. A type system designer defines type qualifiers and their semantics, and a compiler ...

Papi, Matthew M

2008-01-01T23:59:59.000Z

453

Mid America Agri Products | Open Energy Information  

Open Energy Info (EERE)

Mid America Agri Products Mid America Agri Products Jump to: navigation, search Name Mid America Agri Products Place Madrid, Nebraska Zip 69150 Product Ethanol producer located in Madrid, Nebraska. Coordinates 40.4203°, -3.705774° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":40.4203,"lon":-3.705774,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

454

CalStar Products | Open Energy Information  

Open Energy Info (EERE)

CalStar Products CalStar Products Jump to: navigation, search Name CalStar Products Place Newark, California Zip 94560 Product California-based environmentally friendly brick and paving stone manufacturer. Coordinates 44.690435°, -71.951685° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":44.690435,"lon":-71.951685,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

455

RSI Silicon Products LLC | Open Energy Information  

Open Energy Info (EERE)

RSI Silicon Products LLC RSI Silicon Products LLC Jump to: navigation, search Name RSI Silicon Products LLC Place Easton, Massachusetts Zip 18040 Sector Solar Product Early-stage startup which is developing a process for solar-grade silicon manufacture at low energy intensity, spinoff from MIT. Coordinates 47.237806°, -121.179542° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":47.237806,"lon":-121.179542,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

456

Advanced Solar Products | Open Energy Information  

Open Energy Info (EERE)

Products Products Jump to: navigation, search Name Advanced Solar Products Place Flemington, New Jersey Zip 8822 Product New Jersey-based PV systems installer and project developer. Coordinates 39.266175°, -80.132549° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":39.266175,"lon":-80.132549,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

457

CMC/ BMC Utility Products | Open Energy Information  

Open Energy Info (EERE)

CMC/ BMC Utility Products CMC/ BMC Utility Products Jump to: navigation, search Name CMC/ BMC Utility Products Address 3501 Symmes Road Place Hamilton, Ohio Zip 45015 Sector Services, Solar, Wind energy Product Installation; Maintenance and repair;Manufacturing Phone number 513-860-4455 Website http://www.cmclugs.com Coordinates 39.3443592°, -84.5062904° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":39.3443592,"lon":-84.5062904,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

458

Quarkonium production in ultra-relativistic nuclear collisions: suppression vs. enhancement  

E-Print Network (OSTI)

After a brief review of the various scenarios for quarkonium production in ultra-relativistic nucleus-nucleus collisions we focus on the ingredients and assumptions underlying the statistical hadronization model. We then confront model predictions for J/$\\psi$ phase space distributions with the most recent data from the RHIC accelerator. Analysis of the rapidity dependence of the J/$\\psi$ nuclear modification factor yields first evidence for the production of J/$\\psi$ mesons at the phase boundary. We conclude with predictions for charmonium production at the LHC.

P. Braun-Munzinger

2007-01-31T23:59:59.000Z

459

Figure 2. Stratigraphic Summary of Ages, Names and Rock Types in ...  

U.S. Energy Information Administration (EIA)

Figure 2. Stratigraphic Summary of Ages, Names and Rock Types in the ANWR 1002 and Coastal Plain Area of the Alaska North Slope. Potentially Productive ...

460

Table 14. U.S. Propane (Consumer Grade) Prices by Sales Type  

Annual Energy Outlook 2012 (EIA)

and EIA-782B, "Resellers'Retailers' Monthly Petroleum Product Sales Report." 14. U.S. Propane (Consumer Grade) Prices by Sales Type 28 Energy Information Administration ...

Note: This page contains sample records for the topic "assumptions product types" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


461

Practical pluggable types for Java.  

E-Print Network (OSTI)

??This paper introduces the Checker Framework, which supports adding pluggable type systems to the Java language in a backward-compatible way. A type system designer defines… (more)

Papi, Matthew M

2008-01-01T23:59:59.000Z

462

Type inference for datalog with complex type hierarchies  

Science Conference Proceedings (OSTI)

Type inference for Datalog can be understood as the problem of mapping programs to a sublanguage for which containment is decidable. To wit, given a program in Datalog, a schema describing the types of extensional relations, and a user-supplied set of ... Keywords: datalog, type inference, type system

Max Schäfer; Oege de Moor

2010-01-01T23:59:59.000Z

463

Non-LTE Balmer line formation in late-type spectra: Effects of atomic processes involving hydrogen atoms  

E-Print Network (OSTI)

(*** abridged ***) Context: The wings of Balmer lines are often used as effective temperature diagnostics for late-type stars under the assumption they form in local thermodynamic equilibrium. Aims: Our goal is to investigate the non-LTE formation of Balmer lines in late-type stellar atmospheres, to establish if the assumption of LTE is justified. Furthermore, we aim to determine which collision processes are important for the problem; in particular, the role of collision processes with hydrogen atoms is investigated. Method: A model hydrogen atom for non-LTE calculations has been constructed accounting for various collision processes using the best available data from the literature. The processes included are inelastic collisions with electrons and hydrogen atoms, mutual neutralisation and Penning ionisation. Non-LTE calculations are performed, and the relative importance of the collision processes is investigated. Results: Our calculations show electron collisions alone are not sufficient to establish LTE for the formation of Balmer line wings. The role of inelastic collisions with neutral hydrogen is unclear. The available data for these processes is of questionable quality, and different prescriptions for the rate coefficents give significantly different results for the Balmer line wings. Conclusions: Improved calculations or experimental data are needed for excitation and, particularly, ionisation of hydrogen atoms in low-lying states by hydrogen atom impact at near threshold energies. Until such data are available, the assumption of LTE for the formation of Balmer line wings in late-type stars is questionable.

P. S. Barklem

2007-02-08T23:59:59.000Z

464

HEAVY AND THERMAL OIL RECOVERY PRODUCTION MECHANISMS  

Science Conference Proceedings (OSTI)

This technical progress report describes work performed from October 1 through December 31, 2002 , for the project ''Heavy and Thermal Oil Recovery Production Mechanisms.'' In this project, a broad spectrum of research is undertaken related to thermal and heavy-oil recovery. The research tools and techniques used are varied and span from pore-level imaging of multiphase fluid flow to definition of reservoir-scale features through streamline-based history-matching techniques. During this period, experimental data regarding multidimensional imbibition was analyzed to obtain shape factors appropriate for dual-porosity simulation. It is shown that the usual assumption of constant, time-independent shape factors is incorrect. In other work, we continued to study the mechanisms by which oil is produced from fractured media at high pressure and high temperature. High temperature significantly increased the apparent wettability and affected water relative permeability of cores used in previous experiments. A phenomenological and mechanistic cause for this behavior is sought. Our work in the area of primary production of heavy oil continues with field cores and crude oil. On the topic of reservoir definition, work continued on developing techniques that integrate production history into reservoir models using streamline-based properties.

Anthony R. Kovscek

2003-01-01T23:59:59.000Z

465

Observation of Single Top Quark Production  

SciTech Connect

The author reports on the observation of electroweak production of single top quarks in p{bar p} collisions at {radical}s = 1.96 Tev using 2.3 fb{sup -1} of data collected with the D0 detector at the fermilab Tevatron Collider. Using events containing an isolated electron or muon, missing transverse energy, two, three or four jets, with one or two of them identified as originating from the fragmentation of a b quark, the measured cross section for the process p{bar p} {yields} tb + X, tqb + X is 3.94 {+-} 0.88 pb (for a top quark mass of 170 GeV). the probability to measure a cross section at this value or higher in the absence of signal is 2.5 x 10{sup -7}, corresponding to a 5.0 standard deviation significance. Using the same dataset, the measured cross sections for the t- and the s-channel processes when determined simultaneously with no assumption on their relative production rate are 3.14{sub -0.80}{sup +0.94} pb and 1.05 {+-} 0.81 pb respectively, consistent with standard model expectations. The measured t-channel cross section has a significance of 4.8 standard deviations, representing the first evidence for the production of an individual single top process to be detected.

Gerber, Cecilia E.; /Illinois U., Chicago

2009-09-01T23:59:59.000Z

466

Type inference for generic Haskell  

E-Print Network (OSTI)

Abstract. The more expressive a type system, the more type information has to be provided in a program. Having to provide a type is sometimes a pain, but lacking expressivity is often even worse. There is a continuous struggle between expressivity and (type-)verbosity. However, even very expressive type systems allow type inference for parts of a program. Generic Haskell is an extension of Haskell that supports defining generic functions. Generic Haskell assumes that the type of a generic function is explicitly specified. This is often no problem, but sometimes it is rather painful to have to specify a type – in particular for generic functions with many dependencies – and sometimes the specified type can be generalized. In this paper, we identify three type inference problems specific to generic functions, and present (partial) solutions to each of them. 1

Alexey Rodriguez; Johan Jeuring; Andres Löh

2005-01-01T23:59:59.000Z

467

Biological production of products from waste gases  

DOE Patents (OSTI)

A method and apparatus are designed for converting waste gases from industrial processes such as oil refining, and carbon black, coke, ammonia, and methanol production, into useful products. The method includes introducing the waste gases into a bioreactor where they are fermented to various products, such as organic acids, alcohols, hydrogen, single cell protein, and salts of organic acids by anaerobic bacteria within the bioreactor. These valuable end products are then recovered, separated and purified.

Gaddy, James L. (Fayetteville, AR)

2002-01-22T23:59:59.000Z

468

Covered Product Category: Cool Roof Products  

Energy.gov (U.S. Department of Energy (DOE))

FEMP provides acquisition guidance across a variety of product categories, including cool roof products, which are an ENERGY STAR®-qualified product category. Federal laws and executive orders mandate that agencies meet these efficiency requirements in all procurement and acquisition actions that are not specifically exempted by law.

469

Geothermal Energy Production from Low Temperature Resources, Coproduced  

Open Energy Info (EERE)

Energy Production from Low Temperature Resources, Coproduced Energy Production from Low Temperature Resources, Coproduced Fluids from Oil and Gas Wells, and Geopressured Resources Jump to: navigation, search Geothermal ARRA Funded Projects for Geothermal Energy Production from Low Temperature Resources, Coproduced Fluids from Oil and Gas Wells, and Geopressured Resources Loading map... {"format":"googlemaps3","type":"ROADMAP","types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"limit":200,"offset":0,"link":"all","sort":[""],"order":[],"headers":"show","mainlabel":"","intro":"","outro":"","searchlabel":"\u2026

470

Refinery & Blender Net Production of Total Finished Petroleum Products  

U.S. Energy Information Administration (EIA) Indexed Site

& Blender Net Production & Blender Net Production Product: Total Finished Petroleum Products Liquefied Refinery Gases Ethane/Ethylene Ethane Ethylene Propane/Propylene Propane Propylene Normal Butane/Butylene Normal Butane Butylene Isobutane/Isobutylene Isobutane Isobutylene Finished Motor Gasoline Reformulated Gasoline Reformulated Blended w/ Fuel Ethanol Reformulated Other Gasoline Conventional Gasoline Conventional Blended w/ Fuel Ethanol Conventional Blended w/ Fuel Ethanol, Ed55 and Lower Conventional Blended w/ Fuel Ethanol, Greater than Ed55 Conventional Other Finished Aviation Gasoline Kerosene-Type Jet Fuel Kerosene Distillate Fuel Oil Distillate F.O., 15 ppm Sulfur and under Distillate F.O., Greater than 15 ppm to 500 ppm Sulfur Distillate F.O., Greater than 500 ppm Sulfur Residual Fuel Oil Residual Fuel Less Than 0.31 Percent Sulfur Residual Fuel 0.31 to 1.00 Percent Sulfur Residual Fuel Greater Than 1.00 Percent Sulfur Petrochemical Feedstocks Naphtha For Petro. Feed. Use Other Oils For Petro. Feed. Use Special Naphthas Lubricants Waxes Petroleum Coke Marketable Petroleum Coke Catalyst Petroleum Coke Asphalt and Road Oil Still Gas Miscellaneous Products Processing Gain(-) or Loss(+) Period-Unit: Monthly-Thousand Barrels Monthly-Thousand Barrels per Day Annual-Thousand Barrels Annual-Thousand Barrels per Day

471

A type system for CHR  

E-Print Network (OSTI)

We propose a generic type system for the Constraint Handling Rules (CHR), a rewriting rule language for implementing constraint solvers. CHR being a high-level extension of a host language, such as Prolog or Java, this type system is parameterized by the type system of the host language. We show the consistency of the type system for CHR w.r.t. its operational semantics. We also study the case when the host language is a constraint logic programming language, typed with the prescriptive type system we developed in our previous work. In particular, we show the consistency of the resulting type system w.r.t. the extended execution model CLP+CHR. This system is implemented through an extension of our type checker TCLP for constraint logic languages. We report on experimental results about the type-checking of 12 CHR solvers and programs, including TCLP itself.

Emmanuel Coquery; François Fages

2005-01-01T23:59:59.000Z

472

Pluggable type-checking for custom type qualifiers in Java  

E-Print Network (OSTI)

We have created a framework for adding custom type qualifiers to the Javalanguage in a backward-compatible way. The type system designer definesthe qualifiers and creates a compiler plug-in that enforces theirsemantics. ...

Papi, Matthew M.

2007-09-17T23:59:59.000Z

473

WEB RESOURCES: Magnesium Production  

Science Conference Proceedings (OSTI)

Feb 12, 2007 ... Mg Production(Australia).pdf 49.21 KB MgProduction_Australia.mht 81.47 KB Mg Production(Brazil Israel Congo Malaysia).pdf 50.48 KB

474

A Type Driven Theory of Predication with Complex Types  

Science Conference Proceedings (OSTI)

This paper investigates several models of the complex type • which is needed to analyze copredication. Previous accounts are shown to be inadequate and a new account both of • and copredication is proposed. Keywords: categorial interpretation,, coercion,, complex types,, copredication,, dot(•)types, lambda calculus,

Nicholas Asher

2008-09-01T23:59:59.000Z

475

Advanced Energy Products | Open Energy Information  

Open Energy Info (EERE)

Products Products Jump to: navigation, search Name Advanced Energy Products Address 123 C Street Place Davis, CA Zip 95616 Website http://www.advancedenergyprodu Coordinates 38.542214°, -121.743393° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":38.542214,"lon":-121.743393,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

476

American Environmental Products | Open Energy Information  

Open Energy Info (EERE)

Logo: American Environmental Products / Sunwave Lighting Name American Environmental Products / Sunwave Lighting Address Box 18432 Place Boulder, Colorado Zip 80308 Sector Efficiency Product Spectrally Enhanced Energy Efficient Lighting Phone number 303-581-9296 Website http://www.sunwavelighting.net Coordinates 40.0153°, -105.2702° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":40.0153,"lon":-105.2702,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

477

MODIS Land Products Subsets  

NLE Websites -- All DOE Office Websites (Extended Search)

ORNL DAAC MODIS Land Product Subsets MODIS Collection 5 Global Subsetting and Visualization Tool Create subset for user selected site, area, product, and time period. Data for...

478

Production Project Accounts  

NLE Websites -- All DOE Office Websites (Extended Search)

Production Project Accounts Production Project Accounts Overview Most NERSC login accounts are associated with specific individuals and must not be shared. Sometimes it is...

479

from Isotope Production Facility  

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Cancer-fighting treatment gets boost from Isotope Production Facility April 13, 2012 Isotope Production Facility produces cancer-fighting actinium - 2 - 2:32 Isotope cancer...

480

Century Model Product Available  

NLE Websites -- All DOE Office Websites (Extended Search)

Century Model Available The ORNL DAAC announces the availability of a new model product. The model product "CENTURY: Modeling Ecosystem Responses to Climate Change, Version 4...

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