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Title: Optimization under Uncertainty - Scramjet and Power Grid -.

Abstract

Abstract not provided.

Authors:
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1389706
Report Number(s):
SAND2016-8658C
647134
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the SAMSI 2016-2017 Program on Optimization Opening Workshop held August 29 - September 2, 2016 in Raleigh, NC.
Country of Publication:
United States
Language:
English

Citation Formats

Najm, Habib N. Optimization under Uncertainty - Scramjet and Power Grid -.. United States: N. p., 2016. Web.
Najm, Habib N. Optimization under Uncertainty - Scramjet and Power Grid -.. United States.
Najm, Habib N. 2016. "Optimization under Uncertainty - Scramjet and Power Grid -.". United States. doi:. https://www.osti.gov/servlets/purl/1389706.
@article{osti_1389706,
title = {Optimization under Uncertainty - Scramjet and Power Grid -.},
author = {Najm, Habib N.},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month = 9
}

Conference:
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  • Incomplete convergence in numerical simulation such as computational physics simulations and/or Monte Carlo simulations can enter into the calculation of the objective function in an optimization problem, producing noise, bias, and topo- graphical inaccuracy in the objective function. These affect accuracy and convergence rate in the optimization problem. This paper is concerned with global searching of a diverse parameter space, graduating to accelerated local convergence to a (hopefully) global optimum, in a framework that acknowledges convergence uncertainty and manages model resolu- tion to efficiently reduce uncertainty in the final optimum. In its own right, the global-to-local optimization engine employed heremore » (devised for noise tolerance) performs better than other classical and contemporary optimization approaches tried individually and in combination on the "industrial" test problem to be presented.« less
  • Abstract not provided.
  • Pulverized coal (PC) power plants are widely recognized as major water consumers whose operability has started to be affected by drought conditions across some regions of the country. Water availability will further restrict the retrofitting of existing PC plants with water-expensive carbon capture technologies. Therefore, national efforts to reduce water withdrawal and consumption have been intensified. Water consumption in PC plants is strongly associated to losses from the cooling water cycle, particularly water evaporation from cooling towers. Accurate estimation of these water losses requires realistic cooling tower models, as well as the inclusion of uncertainties arising from atmospheric conditions. Inmore » this work, the cooling tower for a supercritical PC power plant was modeled as a humidification operation and used for optimization under uncertainty. Characterization of the uncertainty (air temperature and humidity) was based on available weather data. Process characteristics including boiler conditions, reactant ratios, and pressure ratios in turbines were calculated to obtain the minimum water consumption under the above mentioned uncertainties. In this study, the calculated conditions predicted up to 12% in reduction in the average water consumption for a 548 MW supercritical PC power plant simulated using Aspen Plus. Optimization under uncertainty for these large-scale PC plants cannot be solved with conventional stochastic programming algorithms because of the computational expenses involved. In this work, we discuss the use of a novel better optimization of nonlinear uncertain systems (BONUS) algorithm which dramatically decreases the computational requirements of the stochastic optimization.« less
  • Pulverized coal (PC) power plants are widely recognized as major water consumers whose operability has started to be affected by drought conditions across some regions of the country. Water availability will further restrict the retrofitting of existing PC plants with water-expensive carbon capture technologies. Therefore, national efforts to reduce water withdrawal and consumption have been intensified. Water consumption in PC plants is strongly associated to losses from the cooling water cycle, particularly water evaporation from cooling towers. Accurate estimation of these water losses requires realistic cooling tower models, as well as the inclusion of uncertainties arising from atmospheric conditions. Inmore » this work, the cooling tower for a supercritical PC power plant was modeled as a humidification operation and used for optimization under uncertainty. Characterization of the uncertainty (air temperature and humidity) was based on available weather data. Process characteristics including boiler conditions, reactant ratios, and pressure ratios in turbines were calculated to obtain the minimum water consumption under the above mentioned uncertainties. In this study, the calculated conditions predicted up to 12% in reduction in the average water consumption for a 548 MW supercritical PC power plant simulated using Aspen Plus. Optimization under uncertainty for these large-scale PC plants cannot be solved with conventional stochastic programming algorithms because of the computational expenses involved. In this work, we discuss the use of a novel better optimization of nonlinear uncertain systems (BONUS) algorithm which dramatically decreases the computational requirements of the stochastic optimization.« less