Sensor Network Design Algorithm for Power Plant Efficiency Maximization and Its Application
 Authors:
 Publication Date:
 Research Org.:
 National Energy Technology Lab. (NETL), Pittsburgh, PA, and Morgantown, WV (United States). Inhouse Research
 Sponsoring Org.:
 USDOE Office of Fossil Energy (FE)
 OSTI Identifier:
 1255973
 Report Number(s):
 NETLPUB20382
 Resource Type:
 Conference
 Resource Relation:
 Conference: 59th Annual ISA 2016 POWID Symposium, Charlotte, NC, June 2730 (2016).
 Country of Publication:
 United States
 Language:
 English
 Subject:
 01 COAL, LIGNITE, AND PEAT; 20 FOSSILFUELED POWER PLANTS; 97 MATHEMATICS AND COMPUTING; Sensor Network Design, Efficiency, Optimization, IGCC, Acid Gas Removal, Unscented Kalman Filer
Citation Formats
Bhattacharyya, Debangsu, Turton, Richard, and Zitney, Stephen E. Sensor Network Design Algorithm for Power Plant Efficiency Maximization and Its Application. United States: N. p., 2016.
Web.
Bhattacharyya, Debangsu, Turton, Richard, & Zitney, Stephen E. Sensor Network Design Algorithm for Power Plant Efficiency Maximization and Its Application. United States.
Bhattacharyya, Debangsu, Turton, Richard, and Zitney, Stephen E. 2016.
"Sensor Network Design Algorithm for Power Plant Efficiency Maximization and Its Application". United States.
doi:. https://www.osti.gov/servlets/purl/1255973.
@article{osti_1255973,
title = {Sensor Network Design Algorithm for Power Plant Efficiency Maximization and Its Application},
author = {Bhattacharyya, Debangsu and Turton, Richard and Zitney, Stephen E},
abstractNote = {},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month = 6
}
Other availability
Please see Document Availability for additional information on obtaining the fulltext document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.

Sensor placement algorithm development to maximize the efficiency of acid gas removal unit for integrated gasifiction combined sycle (IGCC) power plant with CO2 capture
Future integrated gasification combined cycle (IGCC) power plants with CO{sub 2} capture will face stricter operational and environmental constraints. Accurate values of relevant states/outputs/disturbances are needed to satisfy these constraints and to maximize the operational efficiency. Unfortunately, a number of these process variables cannot be measured while a number of them can be measured, but have low precision, reliability, or signaltonoise ratio. In this work, a sensor placement (SP) algorithm is developed for optimal selection of sensor location, number, and type that can maximize the plant efficiency and result in a desired precision of the relevant measured/unmeasured states. In thismore » 
Sensor placement algorithm development to maximize the efficiency of acid gas removal unit for integrated gasification combined cycle (IGCC) power plant with CO{sub 2} capture
Future integrated gasification combined cycle (IGCC) power plants with CO{sub 2} capture will face stricter operational and environmental constraints. Accurate values of relevant states/outputs/disturbances are needed to satisfy these constraints and to maximize the operational efficiency. Unfortunately, a number of these process variables cannot be measured while a number of them can be measured, but have low precision, reliability, or signaltonoise ratio. In this work, a sensor placement (SP) algorithm is developed for optimal selection of sensor location, number, and type that can maximize the plant efficiency and result in a desired precision of the relevant measured/unmeasured states. In thismore » 
Nonlinear Dynamic ModelBased Multiobjective Sensor Network Design Algorithm for a Plant with an EstimatorBased Control System
Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimatorbased control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The largescale combinatorial optimizationmore »