Machine-learning-driven feature importance analysis for guiding the protonic ceramic fuel cell manufacturing
The protonic ceramic fuel cell (PCFC) is currently attracting attention as a promising energy-conversion device capable of generating electricity from hydrogen with high efficiency. However, when developing high-performance PCFCs, a wide range of material properties and manufacturing processes must...
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| Language: | English |
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Elsevier
2025-07-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590005625000347 |
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| author | Jinwoo Kim Jaewan Baek Mingi Choi |
| author_facet | Jinwoo Kim Jaewan Baek Mingi Choi |
| author_sort | Jinwoo Kim |
| collection | DOAJ |
| description | The protonic ceramic fuel cell (PCFC) is currently attracting attention as a promising energy-conversion device capable of generating electricity from hydrogen with high efficiency. However, when developing high-performance PCFCs, a wide range of material properties and manufacturing processes must be optimized, necessitating tremendous time and manpower investments as well as a high cost. To address these issues, this study proposes a method by which to analyze the effects of certain materials and manufacturing processes on the fabrication of PCFCs, assisted by machine learning (ML). Based on data from earlier work, we first evaluate the performance-predicting capabilities of 6 ML models, showing the best-predicting performance with XGBoost model. Based on the selected model of XGBoost, we also conduct the feature analysis using Shapley additive explanations, which successfully determine the factors contributing most to the PCFC performance in terms of the materials and manufacturing processes for the anode, cathode, and electrolyte in each case. These results can give us guidelines for the efficient manufacturing of the PCFC. |
| format | Article |
| id | doaj-art-ff459ec7e4654b26bb372e36b6cce5d6 |
| institution | OA Journals |
| issn | 2590-0056 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Array |
| spelling | doaj-art-ff459ec7e4654b26bb372e36b6cce5d62025-08-20T02:07:31ZengElsevierArray2590-00562025-07-012610040710.1016/j.array.2025.100407Machine-learning-driven feature importance analysis for guiding the protonic ceramic fuel cell manufacturingJinwoo Kim0Jaewan Baek1Mingi Choi2Department of Future Energy Convergence, Seoul National University of Science and Technology, Seoul, 01811, South KoreaDepartment of Future Energy Convergence, Seoul National University of Science and Technology, Seoul, 01811, South KoreaCorresponding author.; Department of Future Energy Convergence, Seoul National University of Science and Technology, Seoul, 01811, South KoreaThe protonic ceramic fuel cell (PCFC) is currently attracting attention as a promising energy-conversion device capable of generating electricity from hydrogen with high efficiency. However, when developing high-performance PCFCs, a wide range of material properties and manufacturing processes must be optimized, necessitating tremendous time and manpower investments as well as a high cost. To address these issues, this study proposes a method by which to analyze the effects of certain materials and manufacturing processes on the fabrication of PCFCs, assisted by machine learning (ML). Based on data from earlier work, we first evaluate the performance-predicting capabilities of 6 ML models, showing the best-predicting performance with XGBoost model. Based on the selected model of XGBoost, we also conduct the feature analysis using Shapley additive explanations, which successfully determine the factors contributing most to the PCFC performance in terms of the materials and manufacturing processes for the anode, cathode, and electrolyte in each case. These results can give us guidelines for the efficient manufacturing of the PCFC.http://www.sciencedirect.com/science/article/pii/S2590005625000347Machine-learningProtonic ceramic fuel cellsFeature-importance analysis |
| spellingShingle | Jinwoo Kim Jaewan Baek Mingi Choi Machine-learning-driven feature importance analysis for guiding the protonic ceramic fuel cell manufacturing Array Machine-learning Protonic ceramic fuel cells Feature-importance analysis |
| title | Machine-learning-driven feature importance analysis for guiding the protonic ceramic fuel cell manufacturing |
| title_full | Machine-learning-driven feature importance analysis for guiding the protonic ceramic fuel cell manufacturing |
| title_fullStr | Machine-learning-driven feature importance analysis for guiding the protonic ceramic fuel cell manufacturing |
| title_full_unstemmed | Machine-learning-driven feature importance analysis for guiding the protonic ceramic fuel cell manufacturing |
| title_short | Machine-learning-driven feature importance analysis for guiding the protonic ceramic fuel cell manufacturing |
| title_sort | machine learning driven feature importance analysis for guiding the protonic ceramic fuel cell manufacturing |
| topic | Machine-learning Protonic ceramic fuel cells Feature-importance analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2590005625000347 |
| work_keys_str_mv | AT jinwookim machinelearningdrivenfeatureimportanceanalysisforguidingtheprotonicceramicfuelcellmanufacturing AT jaewanbaek machinelearningdrivenfeatureimportanceanalysisforguidingtheprotonicceramicfuelcellmanufacturing AT mingichoi machinelearningdrivenfeatureimportanceanalysisforguidingtheprotonicceramicfuelcellmanufacturing |