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

dc.contributor.author Bankole, Temitayo
dc.contributor.author Bhattacharyya, Debangsu
dc.contributor.author Pezzini, Paolo
dc.contributor.author Gebreslassie, Berhane
dc.contributor.author Harun, Nor
dc.contributor.author Tucker, David
dc.contributor.author Diwekar, Urmila
dc.contributor.author Bryden, Kenneth
dc.contributor.department Ames Laboratory
dc.contributor.department Mechanical Engineering
dc.date 2021-01-14T02:25:08.000
dc.date.accessioned 2021-02-24T20:28:33Z
dc.date.available 2021-02-24T20:28:33Z
dc.date.issued 2020-11-02
dc.description.abstract <p>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. 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.</p>
dc.identifier archive/lib.dr.iastate.edu/ameslab_manuscripts/795/
dc.identifier.articleid 1796
dc.identifier.contextkey 21067104
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ameslab_manuscripts/795
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/93238
dc.language.iso en
dc.relation.ispartofseries IS-J 10381
dc.source.bitstream archive/lib.dr.iastate.edu/ameslab_manuscripts/795/IS_J_10381.pdf|||Sat Jan 15 01:56:30 UTC 2022
dc.source.uri 10.1021/acs.iecr.0c02865
dc.subject.disciplines Chemical Engineering
dc.subject.disciplines Energy Systems
dc.subject.disciplines Materials Chemistry
dc.subject.keywords Algorithms
dc.subject.keywords Atmospheric chemistry
dc.subject.keywords Optimization
dc.subject.keywords Power
dc.subject.keywords Plenum
dc.title Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isOrgUnitOfPublication 25913818-6714-4be5-89a6-f70c8facdf7e
relation.isOrgUnitOfPublication 6d38ab0f-8cc2-4ad3-90b1-67a60c5a6f59
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