Numerical investigation of micro solid oxide fuel cell performance in combination with artificial intelligence approach
The current study presents a multiphysics numerical model for a micro-planar proton-conducting solid oxide fuel cell (H-SOFC). The numerical model considered an anode-supported H-SOFC with direct internal reforming (DIR) of methane. The model solves coupled nonlinear equations, including continuity,...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024170272 |
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| author | Parastoo Taleghani Majid Ghassemi Mahmoud Chizari |
| author_facet | Parastoo Taleghani Majid Ghassemi Mahmoud Chizari |
| author_sort | Parastoo Taleghani |
| collection | DOAJ |
| description | The current study presents a multiphysics numerical model for a micro-planar proton-conducting solid oxide fuel cell (H-SOFC). The numerical model considered an anode-supported H-SOFC with direct internal reforming (DIR) of methane. The model solves coupled nonlinear equations, including continuity, momentum, mass transfer, chemical and electrochemical reactions, and energy equations. Furthermore, The numerical model results are used in artificial intelligence (AI) models, the K-nearest neighbour (KNN) and, artificial neural network (ANN), to predict the current density and power density of the H-SOFC. The results show that increasing the air-to-fuel (A/F) ratio decreases the current density and overall cell power. In particular, improvements in power and current density observed in H-SOFC when the A/F ratio is set to 0.5, resulting in a respective increase of 2 % and 7 % compared to the initial state at A/F = 1. With an error rate of less than 1 % and an R-score of around 99 %, the ANN model shows good agreement with the numerical results. |
| format | Article |
| id | doaj-art-2f1e714ecbca4e14b3ba73a6b1755efd |
| institution | OA Journals |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-2f1e714ecbca4e14b3ba73a6b1755efd2025-08-20T02:35:00ZengElsevierHeliyon2405-84402024-12-011024e4099610.1016/j.heliyon.2024.e40996Numerical investigation of micro solid oxide fuel cell performance in combination with artificial intelligence approachParastoo Taleghani0Majid Ghassemi1Mahmoud Chizari2Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, IranDepartment of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran; Corresponding author. Department of Mechanical Engineering, K. N. Toosi University of Technology, Iran.School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, UK; Corresponding author. School of Physics, Engineering and Computer Science, University of Hertfordshire, UK.The current study presents a multiphysics numerical model for a micro-planar proton-conducting solid oxide fuel cell (H-SOFC). The numerical model considered an anode-supported H-SOFC with direct internal reforming (DIR) of methane. The model solves coupled nonlinear equations, including continuity, momentum, mass transfer, chemical and electrochemical reactions, and energy equations. Furthermore, The numerical model results are used in artificial intelligence (AI) models, the K-nearest neighbour (KNN) and, artificial neural network (ANN), to predict the current density and power density of the H-SOFC. The results show that increasing the air-to-fuel (A/F) ratio decreases the current density and overall cell power. In particular, improvements in power and current density observed in H-SOFC when the A/F ratio is set to 0.5, resulting in a respective increase of 2 % and 7 % compared to the initial state at A/F = 1. With an error rate of less than 1 % and an R-score of around 99 %, the ANN model shows good agreement with the numerical results.http://www.sciencedirect.com/science/article/pii/S2405844024170272Micro solid oxide fuel cellProton-conducting electrolyteNumerical modelArtificial intelligenceArtificial neural network |
| spellingShingle | Parastoo Taleghani Majid Ghassemi Mahmoud Chizari Numerical investigation of micro solid oxide fuel cell performance in combination with artificial intelligence approach Heliyon Micro solid oxide fuel cell Proton-conducting electrolyte Numerical model Artificial intelligence Artificial neural network |
| title | Numerical investigation of micro solid oxide fuel cell performance in combination with artificial intelligence approach |
| title_full | Numerical investigation of micro solid oxide fuel cell performance in combination with artificial intelligence approach |
| title_fullStr | Numerical investigation of micro solid oxide fuel cell performance in combination with artificial intelligence approach |
| title_full_unstemmed | Numerical investigation of micro solid oxide fuel cell performance in combination with artificial intelligence approach |
| title_short | Numerical investigation of micro solid oxide fuel cell performance in combination with artificial intelligence approach |
| title_sort | numerical investigation of micro solid oxide fuel cell performance in combination with artificial intelligence approach |
| topic | Micro solid oxide fuel cell Proton-conducting electrolyte Numerical model Artificial intelligence Artificial neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024170272 |
| work_keys_str_mv | AT parastootaleghani numericalinvestigationofmicrosolidoxidefuelcellperformanceincombinationwithartificialintelligenceapproach AT majidghassemi numericalinvestigationofmicrosolidoxidefuelcellperformanceincombinationwithartificialintelligenceapproach AT mahmoudchizari numericalinvestigationofmicrosolidoxidefuelcellperformanceincombinationwithartificialintelligenceapproach |