Exergy analysis of a fuel cell power system and optimizing it with Fractional-order Coyote Optimization Algorithm

This study presents an exergy assessment methodology for a power production system defined by a high-temperature proton exchange membrane fuel cell. The evaluated structure has an organic Rankine cycle for recovering the lost heat. This study provides an optimum balanced model by optimizing the vari...

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Autores principales:	Li Sun, Xue-Feng Han, Yi-Peng Xu, Navid Razmjooy
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	High-temperature proton exchange membrane fuel cell Fractional-order Coyote Optimization Algorithm Work Exergy Irreversibility Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/c01619e7f11041d6986688b05b583bc4
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id	oai:doaj.org-article:c01619e7f11041d6986688b05b583bc4
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spelling	oai:doaj.org-article:c01619e7f11041d6986688b05b583bc42021-11-14T04:34:20ZExergy analysis of a fuel cell power system and optimizing it with Fractional-order Coyote Optimization Algorithm2352-484710.1016/j.egyr.2021.10.098https://doaj.org/article/c01619e7f11041d6986688b05b583bc42021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721011173https://doaj.org/toc/2352-4847This study presents an exergy assessment methodology for a power production system defined by a high-temperature proton exchange membrane fuel cell. The evaluated structure has an organic Rankine cycle for recovering the lost heat. This study provides an optimum balanced model by optimizing the variables of the system. Here, a new improved metaheuristic, called Fractional-order Coyote Optimization Algorithm is proposed to the studied system to provide results with higher accuracy and precision. Three cost functions have been utilized for optimization: irreversibility, work, and exergy. Simulation results of the proposed method are then implemented in a case study and its results are validated by comparing with experimental data, the original COA, and the Genetic Algorithm (GA) from the literature. Final achievements indicate that the proposed algorithm gives the highest confirmation by the experimental data.Li SunXue-Feng HanYi-Peng XuNavid RazmjooyElsevierarticleHigh-temperature proton exchange membrane fuel cellFractional-order Coyote Optimization AlgorithmWorkExergyIrreversibilityElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 7424-7433 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	High-temperature proton exchange membrane fuel cell Fractional-order Coyote Optimization Algorithm Work Exergy Irreversibility Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	High-temperature proton exchange membrane fuel cell Fractional-order Coyote Optimization Algorithm Work Exergy Irreversibility Electrical engineering. Electronics. Nuclear engineering TK1-9971 Li Sun Xue-Feng Han Yi-Peng Xu Navid Razmjooy Exergy analysis of a fuel cell power system and optimizing it with Fractional-order Coyote Optimization Algorithm
description	This study presents an exergy assessment methodology for a power production system defined by a high-temperature proton exchange membrane fuel cell. The evaluated structure has an organic Rankine cycle for recovering the lost heat. This study provides an optimum balanced model by optimizing the variables of the system. Here, a new improved metaheuristic, called Fractional-order Coyote Optimization Algorithm is proposed to the studied system to provide results with higher accuracy and precision. Three cost functions have been utilized for optimization: irreversibility, work, and exergy. Simulation results of the proposed method are then implemented in a case study and its results are validated by comparing with experimental data, the original COA, and the Genetic Algorithm (GA) from the literature. Final achievements indicate that the proposed algorithm gives the highest confirmation by the experimental data.
format	article
author	Li Sun Xue-Feng Han Yi-Peng Xu Navid Razmjooy
author_facet	Li Sun Xue-Feng Han Yi-Peng Xu Navid Razmjooy
author_sort	Li Sun
title	Exergy analysis of a fuel cell power system and optimizing it with Fractional-order Coyote Optimization Algorithm
title_short	Exergy analysis of a fuel cell power system and optimizing it with Fractional-order Coyote Optimization Algorithm
title_full	Exergy analysis of a fuel cell power system and optimizing it with Fractional-order Coyote Optimization Algorithm
title_fullStr	Exergy analysis of a fuel cell power system and optimizing it with Fractional-order Coyote Optimization Algorithm
title_full_unstemmed	Exergy analysis of a fuel cell power system and optimizing it with Fractional-order Coyote Optimization Algorithm
title_sort	exergy analysis of a fuel cell power system and optimizing it with fractional-order coyote optimization algorithm
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/c01619e7f11041d6986688b05b583bc4
work_keys_str_mv	AT lisun exergyanalysisofafuelcellpowersystemandoptimizingitwithfractionalordercoyoteoptimizationalgorithm AT xuefenghan exergyanalysisofafuelcellpowersystemandoptimizingitwithfractionalordercoyoteoptimizationalgorithm AT yipengxu exergyanalysisofafuelcellpowersystemandoptimizingitwithfractionalordercoyoteoptimizationalgorithm AT navidrazmjooy exergyanalysisofafuelcellpowersystemandoptimizingitwithfractionalordercoyoteoptimizationalgorithm
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Exergy analysis of a fuel cell power system and optimizing it with Fractional-order Coyote Optimization Algorithm

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