Finding best operational conditions of PEM fuel cell using adaptive neuro-fuzzy inference system and metaheuristics

Hegazy Rezk; Tabbi Wilberforce; Enas Taha Sayed; Ahmed N.M. Alahmadi; A. G. Olabi

doi:10.1016/j.egyr.2022.04.061

Finding best operational conditions of PEM fuel cell using adaptive neuro-fuzzy inference system and metaheuristics

Hegazy Rezk^*, Tabbi Wilberforce, Enas Taha Sayed, Ahmed N.M. Alahmadi, A. G. Olabi

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

The optimum output power of the proton exchange membrane fuel cell (PEMFC) is dependent on operational conditions such as fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate. Therefore, the aim of this paper is to enhance performance of PEMFC by identifying optimal operating parameters of PEMFC. The proposed strategy includes both modelling and optimization stages. An adaptive network-based fuzzy inference system (ANFIS) is utilized in creating the model based on experimental datasets. Whereas, the grey wolf optimizer (GWO) is used to identify the best values of fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate corresponding to maximum power PEMFC. The obtained results demonstrated the superiority of the integration between ANIFS based modelling and GWO. Regarding the modelling accuracy, The RMSE values are 0.017 as well as 0.0262 respectively for treating and testing phases. The coefficient of determination values is 0.9921 as well as 0.9622 respectively for treating coupled with testing phases. The optimal parameters are 1.0 bar, 0.8 bar, 117.03 mL/min, 150.0 mL/min respectively fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate corresponding to maximum power of PEMFC. Thanks to the integration between ANFIS-based modelling and GWO, the output power of PEMFC has been increased from 0.587 W using experimental work to 0.92 W.

Original language	English
Pages (from-to)	6181-6190
Number of pages	10
Journal	Energy Reports
Volume	8
Early online date	12 May 2022
DOIs	https://doi.org/10.1016/j.egyr.2022.04.061
Publication status	Published - Nov 2022

Bibliographical note

© 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license 4.0

Funding Information:
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number ( IF-PSAU-2021/01/17835 ).

Keywords

ANFIS modelling
Fuel cells
Grey wolf optimizer

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10.1016/j.egyr.2022.04.061Licence: CC BY-NC-ND 4.0

Finding best operational conditions of PEM fuel cell
© 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license 4.0
Final published version, 4.13 MBLicence: CC BY-NC-ND 4.0

Cite this

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title = "Finding best operational conditions of PEM fuel cell using adaptive neuro-fuzzy inference system and metaheuristics",

abstract = "The optimum output power of the proton exchange membrane fuel cell (PEMFC) is dependent on operational conditions such as fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate. Therefore, the aim of this paper is to enhance performance of PEMFC by identifying optimal operating parameters of PEMFC. The proposed strategy includes both modelling and optimization stages. An adaptive network-based fuzzy inference system (ANFIS) is utilized in creating the model based on experimental datasets. Whereas, the grey wolf optimizer (GWO) is used to identify the best values of fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate corresponding to maximum power PEMFC. The obtained results demonstrated the superiority of the integration between ANIFS based modelling and GWO. Regarding the modelling accuracy, The RMSE values are 0.017 as well as 0.0262 respectively for treating and testing phases. The coefficient of determination values is 0.9921 as well as 0.9622 respectively for treating coupled with testing phases. The optimal parameters are 1.0 bar, 0.8 bar, 117.03 mL/min, 150.0 mL/min respectively fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate corresponding to maximum power of PEMFC. Thanks to the integration between ANFIS-based modelling and GWO, the output power of PEMFC has been increased from 0.587 W using experimental work to 0.92 W.",

keywords = "ANFIS modelling, Fuel cells, Grey wolf optimizer",

author = "Hegazy Rezk and Tabbi Wilberforce and Sayed, {Enas Taha} and Alahmadi, {Ahmed N.M.} and Olabi, {A. G.}",

note = "{\textcopyright} 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license 4.0 Funding Information: The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number ( IF-PSAU-2021/01/17835 ). ",

year = "2022",

month = nov,

doi = "10.1016/j.egyr.2022.04.061",

language = "English",

volume = "8",

pages = "6181--6190",

journal = "Energy Reports",

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T1 - Finding best operational conditions of PEM fuel cell using adaptive neuro-fuzzy inference system and metaheuristics

AU - Rezk, Hegazy

AU - Wilberforce, Tabbi

AU - Sayed, Enas Taha

AU - Alahmadi, Ahmed N.M.

AU - Olabi, A. G.

N1 - © 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license 4.0 Funding Information: The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number ( IF-PSAU-2021/01/17835 ).

PY - 2022/11

Y1 - 2022/11

N2 - The optimum output power of the proton exchange membrane fuel cell (PEMFC) is dependent on operational conditions such as fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate. Therefore, the aim of this paper is to enhance performance of PEMFC by identifying optimal operating parameters of PEMFC. The proposed strategy includes both modelling and optimization stages. An adaptive network-based fuzzy inference system (ANFIS) is utilized in creating the model based on experimental datasets. Whereas, the grey wolf optimizer (GWO) is used to identify the best values of fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate corresponding to maximum power PEMFC. The obtained results demonstrated the superiority of the integration between ANIFS based modelling and GWO. Regarding the modelling accuracy, The RMSE values are 0.017 as well as 0.0262 respectively for treating and testing phases. The coefficient of determination values is 0.9921 as well as 0.9622 respectively for treating coupled with testing phases. The optimal parameters are 1.0 bar, 0.8 bar, 117.03 mL/min, 150.0 mL/min respectively fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate corresponding to maximum power of PEMFC. Thanks to the integration between ANFIS-based modelling and GWO, the output power of PEMFC has been increased from 0.587 W using experimental work to 0.92 W.

AB - The optimum output power of the proton exchange membrane fuel cell (PEMFC) is dependent on operational conditions such as fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate. Therefore, the aim of this paper is to enhance performance of PEMFC by identifying optimal operating parameters of PEMFC. The proposed strategy includes both modelling and optimization stages. An adaptive network-based fuzzy inference system (ANFIS) is utilized in creating the model based on experimental datasets. Whereas, the grey wolf optimizer (GWO) is used to identify the best values of fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate corresponding to maximum power PEMFC. The obtained results demonstrated the superiority of the integration between ANIFS based modelling and GWO. Regarding the modelling accuracy, The RMSE values are 0.017 as well as 0.0262 respectively for treating and testing phases. The coefficient of determination values is 0.9921 as well as 0.9622 respectively for treating coupled with testing phases. The optimal parameters are 1.0 bar, 0.8 bar, 117.03 mL/min, 150.0 mL/min respectively fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate corresponding to maximum power of PEMFC. Thanks to the integration between ANFIS-based modelling and GWO, the output power of PEMFC has been increased from 0.587 W using experimental work to 0.92 W.

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KW - Fuel cells

KW - Grey wolf optimizer

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VL - 8

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EP - 6190

JO - Energy Reports

JF - Energy Reports

ER -

Finding best operational conditions of PEM fuel cell using adaptive neuro-fuzzy inference system and metaheuristics

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Keywords

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