Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform
Abstract
Hybrid gas turbine–fuel cell systems have immense potential for high efficiency in electrical power generation with cleaner emissions compared with fossil-fueled power generation. We report a systematic controlled variable (CV) selection method is deployed for a hybrid gas turbine–fuel cell system in the HyPer (hybrid performance) facility at the U.S. Department of Energy’s National Energy Technology Laboratory (NETL) for maximizing its economic and control performance. A three-stage approach is used for the CV selection comprising a priori analysis, multiobjective optimization, and a posteriori analysis. The a priori analysis helps to screen off several candidate CVs, thus reducing the size of the combinatorial optimization problem for multiobjective CV selection. For optimal CV selection, a transfer function model of the HyPer facility is identified. By considering several candidate models, the final transfer function model is selected using Akaike’s Final Prediction Error criterion. Experimental data from the HyPer facility are used to estimate the noise in the measurement data. For solving the combinatorial multiobjective optimization problem for CV selection, a multiagent optimization platform comprising simulated annealing, genetic algorithm, and efficient ant colony optimization algorithms is used. Pareto-optimal CV sets exhibit a high trade-off between the economic and control objective. The a posteriori analysismore »
- Authors:
-
[2];
[4];
- West Virginia Univ., Morgantown, WV (United States); Dassault Systèmes, Wayne, PA (United States)
- West Virginia Univ., Morgantown, WV (United States)
- Ames Lab., Ames, IA (United States); National Energy Technology Lab. (NETL), Morgantown, WV (United States)
- Vishwamitra Research Inst., Chicago, IL (United States)
- National Energy Technology Lab. (NETL), Morgantown, WV (United States)
- Ames Lab., Ames, IA (United States); Iowa State Univ., Ames, IA (United States)
- Publication Date:
- Research Org.:
- Ames Laboratory (AMES), Ames, IA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1756046
- Report Number(s):
- IS-J-10,381
Journal ID: ISSN 0888-5885
- Grant/Contract Number:
- AC02-07CH11358; FE0012451
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Industrial and Engineering Chemistry Research
- Additional Journal Information:
- Journal Volume: 59; Journal Issue: 45; Journal ID: ISSN 0888-5885
- Publisher:
- American Chemical Society (ACS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Algorithms; Atmospheric chemistry; Optimization; Power; Plenum
Citation Formats
Bankole, Temitayo, Bhattacharyya, Debangsu, Pezzini, Paolo, Gebreslassie, Berhane, Harun, Nor Farida, Tucker, David, Diwekar, Urmila, and Bryden, Kenneth M. Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform. United States: N. p., 2020.
Web. doi:10.1021/acs.iecr.0c02865.
Bankole, Temitayo, Bhattacharyya, Debangsu, Pezzini, Paolo, Gebreslassie, Berhane, Harun, Nor Farida, Tucker, David, Diwekar, Urmila, & Bryden, Kenneth M. Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform. United States. https://doi.org/10.1021/acs.iecr.0c02865
Bankole, Temitayo, Bhattacharyya, Debangsu, Pezzini, Paolo, Gebreslassie, Berhane, Harun, Nor Farida, Tucker, David, Diwekar, Urmila, and Bryden, Kenneth M. Mon .
"Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform". United States. https://doi.org/10.1021/acs.iecr.0c02865. https://www.osti.gov/servlets/purl/1756046.
@article{osti_1756046,
title = {Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform},
author = {Bankole, Temitayo and Bhattacharyya, Debangsu and Pezzini, Paolo and Gebreslassie, Berhane and Harun, Nor Farida and Tucker, David and Diwekar, Urmila and Bryden, Kenneth M.},
abstractNote = {Hybrid gas turbine–fuel cell systems have immense potential for high efficiency in electrical power generation with cleaner emissions compared with fossil-fueled power generation. We report a systematic controlled variable (CV) selection method is deployed for a hybrid gas turbine–fuel cell system in the HyPer (hybrid performance) facility at the U.S. Department of Energy’s National Energy Technology Laboratory (NETL) for maximizing its economic and control performance. A three-stage approach is used for the CV selection comprising a priori analysis, multiobjective optimization, and a posteriori analysis. The a priori analysis helps to screen off several candidate CVs, thus reducing the size of the combinatorial optimization problem for multiobjective CV selection. For optimal CV selection, a transfer function model of the HyPer facility is identified. By considering several candidate models, the final transfer function model is selected using Akaike’s Final Prediction Error criterion. Experimental data from the HyPer facility are used to estimate the noise in the measurement data. For solving the combinatorial multiobjective optimization problem for CV selection, a multiagent optimization platform comprising simulated annealing, genetic algorithm, and efficient ant colony optimization algorithms is used. Pareto-optimal CV sets exhibit a high trade-off between the economic and control objective. The a posteriori analysis is undertaken for several top Pareto-optimal CV sets. An optimal CV set is selected that shows the best compromise between process economics and controllability under both nominal and off-design conditions.},
doi = {10.1021/acs.iecr.0c02865},
journal = {Industrial and Engineering Chemistry Research},
number = 45,
volume = 59,
place = {United States},
year = {Mon Nov 02 00:00:00 EST 2020},
month = {Mon Nov 02 00:00:00 EST 2020}
}
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