Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform

Bankole, Temitayo; Bhattacharyya, Debangsu; Pezzini, Paolo; Gebreslassie, Berhane; Harun, Nor Farida; Tucker, David; Diwekar, Urmila; Bryden, Kenneth M.

doi:10.1021/acs.iecr.0c02865

Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform

Journal Article · Sun Nov 01 23:00:00 EST 2020 · Industrial and Engineering Chemistry Research

DOI:https://doi.org/10.1021/acs.iecr.0c02865· OSTI ID:1756046

Bankole, Temitayo ^[1]; ^[2]; Pezzini, Paolo ^[3]; Gebreslassie, Berhane ^[4]; Harun, Nor Farida ^[5]; Tucker, David ^[5]; ^[4]; Bryden, Kenneth M. ^[6]

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)

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.

View Accepted Manuscript (DOE)

Research Organization:: Ames Laboratory (AMES), Ames, IA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC02-07CH11358; FE0012451

OSTI ID:: 1756046

Report Number(s):: IS--J-10,381

Journal Information:: Industrial and Engineering Chemistry Research, Journal Name: Industrial and Engineering Chemistry Research Journal Issue: 45 Vol. 59; ISSN 0888-5885

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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