Optimization of PEMFC Model Parameters Using Meta-Heuristics

The present study introduces an economical–functional design for a polymer electrolyte membrane fuel cell system. To do so, after introducing the optimization problem and solving the problem based on the presented equations in the fuel cell, a cost model is presented. The final design is employed fo...

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Autores principales:	Saeideh Mahdinia, Mehrdad Rezaie, Marischa Elveny, Noradin Ghadimi, Navid Razmjooy
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	fuel cell electrochemical–mechanical model collective animal behavior algorithm improved sensitivity analysis Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/f0830901a0f94457af932e70e6d00f14
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spelling	oai:doaj.org-article:f0830901a0f94457af932e70e6d00f142021-11-25T19:04:14ZOptimization of PEMFC Model Parameters Using Meta-Heuristics10.3390/su1322127712071-1050https://doaj.org/article/f0830901a0f94457af932e70e6d00f142021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12771https://doaj.org/toc/2071-1050The present study introduces an economical–functional design for a polymer electrolyte membrane fuel cell system. To do so, after introducing the optimization problem and solving the problem based on the presented equations in the fuel cell, a cost model is presented. The final design is employed for minimizing the construction cost of a 50 kW fuel cell stack, along with the costs of accessories regarding the current density, stoichiometric coefficient of the hydrogen and air, and pressure of the system as well as the temperature of the system as optimization parameters. The functional–economic model is developed for the studied system in which all components of the system are modeled economically as well as electrochemically–mechanically. The objective function is solved by a newly improved metaheuristic technique, called converged collective animal behavior (CCAB) optimizer. The final results of the method are compared with the standard CAB optimizer and genetic algorithm as a popular technique. The results show that the best optimal cost with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.1061</mn><mo> </mo><mi>$</mi><mo>/</mo><mi>kWh</mi></mrow></semantics></math></inline-formula> is achieved by the CCAB. Finally, a sensitivity analysis is provided for analyzing the consistency of the method.Saeideh MahdiniaMehrdad RezaieMarischa ElvenyNoradin GhadimiNavid RazmjooyMDPI AGarticlefuel cellelectrochemical–mechanical modelcollective animal behavior algorithmimprovedsensitivity analysisEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12771, p 12771 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fuel cell electrochemical–mechanical model collective animal behavior algorithm improved sensitivity analysis Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	fuel cell electrochemical–mechanical model collective animal behavior algorithm improved sensitivity analysis Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Saeideh Mahdinia Mehrdad Rezaie Marischa Elveny Noradin Ghadimi Navid Razmjooy Optimization of PEMFC Model Parameters Using Meta-Heuristics
description	The present study introduces an economical–functional design for a polymer electrolyte membrane fuel cell system. To do so, after introducing the optimization problem and solving the problem based on the presented equations in the fuel cell, a cost model is presented. The final design is employed for minimizing the construction cost of a 50 kW fuel cell stack, along with the costs of accessories regarding the current density, stoichiometric coefficient of the hydrogen and air, and pressure of the system as well as the temperature of the system as optimization parameters. The functional–economic model is developed for the studied system in which all components of the system are modeled economically as well as electrochemically–mechanically. The objective function is solved by a newly improved metaheuristic technique, called converged collective animal behavior (CCAB) optimizer. The final results of the method are compared with the standard CAB optimizer and genetic algorithm as a popular technique. The results show that the best optimal cost with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.1061</mn><mo> </mo><mi>$</mi><mo>/</mo><mi>kWh</mi></mrow></semantics></math></inline-formula> is achieved by the CCAB. Finally, a sensitivity analysis is provided for analyzing the consistency of the method.
format	article
author	Saeideh Mahdinia Mehrdad Rezaie Marischa Elveny Noradin Ghadimi Navid Razmjooy
author_facet	Saeideh Mahdinia Mehrdad Rezaie Marischa Elveny Noradin Ghadimi Navid Razmjooy
author_sort	Saeideh Mahdinia
title	Optimization of PEMFC Model Parameters Using Meta-Heuristics
title_short	Optimization of PEMFC Model Parameters Using Meta-Heuristics
title_full	Optimization of PEMFC Model Parameters Using Meta-Heuristics
title_fullStr	Optimization of PEMFC Model Parameters Using Meta-Heuristics
title_full_unstemmed	Optimization of PEMFC Model Parameters Using Meta-Heuristics
title_sort	optimization of pemfc model parameters using meta-heuristics
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f0830901a0f94457af932e70e6d00f14
work_keys_str_mv	AT saeidehmahdinia optimizationofpemfcmodelparametersusingmetaheuristics AT mehrdadrezaie optimizationofpemfcmodelparametersusingmetaheuristics AT marischaelveny optimizationofpemfcmodelparametersusingmetaheuristics AT noradinghadimi optimizationofpemfcmodelparametersusingmetaheuristics AT navidrazmjooy optimizationofpemfcmodelparametersusingmetaheuristics
_version_	1718410375124221952

Optimization of PEMFC Model Parameters Using Meta-Heuristics

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