Electrode informatics accelerated the optimization of key catalyst layer parameters in direct methanol fuel cells
As the core component of direct methanol fuel cells, the catalyst layer plays the key role as a species, proton and electron transport channel. However, due to the complexity of the system, optimizing its performance involves a large number of experiments and high costs. In this study, finite elemen...
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| Published in: | Nanoscale Vol. 17; no. 2; p. 864 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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England
02.01.2025
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| ISSN: | 2040-3372, 2040-3372 |
| Online Access: | Get more information |
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| Abstract | As the core component of direct methanol fuel cells, the catalyst layer plays the key role as a species, proton and electron transport channel. However, due to the complexity of the system, optimizing its performance involves a large number of experiments and high costs. In this study, finite element simulation combined with machine learning model was constructed to accelerate power density prediction and evaluate the influence of catalyst layer parameters on the maximum power density of direct methanol fuel cells. We built a fuel cell simulation model corresponding to different parameters, obtaining a database of more than 200 sets of 19 eigenvalues, and then used different machine learning models for training and prediction. Finally, three tree-integration methods were selected to rank the importance of 19 characteristic parameters. In addition, we performed a high-throughput screening of 200 000 different parameter combinations based on sequential model-based algorithm configuration. We selected the top 10 parameter combinations with high expected improvement scores and employed them into a numerical simulation model. The results show that a majority of the polarization curves obtained from the top combinations exceed the maximum power density of the original database. This method greatly saves the time of collecting fuel cell data for experiments and speeds up the parameter optimization process. |
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| AbstractList | As the core component of direct methanol fuel cells, the catalyst layer plays the key role as a species, proton and electron transport channel. However, due to the complexity of the system, optimizing its performance involves a large number of experiments and high costs. In this study, finite element simulation combined with machine learning model was constructed to accelerate power density prediction and evaluate the influence of catalyst layer parameters on the maximum power density of direct methanol fuel cells. We built a fuel cell simulation model corresponding to different parameters, obtaining a database of more than 200 sets of 19 eigenvalues, and then used different machine learning models for training and prediction. Finally, three tree-integration methods were selected to rank the importance of 19 characteristic parameters. In addition, we performed a high-throughput screening of 200 000 different parameter combinations based on sequential model-based algorithm configuration. We selected the top 10 parameter combinations with high expected improvement scores and employed them into a numerical simulation model. The results show that a majority of the polarization curves obtained from the top combinations exceed the maximum power density of the original database. This method greatly saves the time of collecting fuel cell data for experiments and speeds up the parameter optimization process.As the core component of direct methanol fuel cells, the catalyst layer plays the key role as a species, proton and electron transport channel. However, due to the complexity of the system, optimizing its performance involves a large number of experiments and high costs. In this study, finite element simulation combined with machine learning model was constructed to accelerate power density prediction and evaluate the influence of catalyst layer parameters on the maximum power density of direct methanol fuel cells. We built a fuel cell simulation model corresponding to different parameters, obtaining a database of more than 200 sets of 19 eigenvalues, and then used different machine learning models for training and prediction. Finally, three tree-integration methods were selected to rank the importance of 19 characteristic parameters. In addition, we performed a high-throughput screening of 200 000 different parameter combinations based on sequential model-based algorithm configuration. We selected the top 10 parameter combinations with high expected improvement scores and employed them into a numerical simulation model. The results show that a majority of the polarization curves obtained from the top combinations exceed the maximum power density of the original database. This method greatly saves the time of collecting fuel cell data for experiments and speeds up the parameter optimization process. As the core component of direct methanol fuel cells, the catalyst layer plays the key role as a species, proton and electron transport channel. However, due to the complexity of the system, optimizing its performance involves a large number of experiments and high costs. In this study, finite element simulation combined with machine learning model was constructed to accelerate power density prediction and evaluate the influence of catalyst layer parameters on the maximum power density of direct methanol fuel cells. We built a fuel cell simulation model corresponding to different parameters, obtaining a database of more than 200 sets of 19 eigenvalues, and then used different machine learning models for training and prediction. Finally, three tree-integration methods were selected to rank the importance of 19 characteristic parameters. In addition, we performed a high-throughput screening of 200 000 different parameter combinations based on sequential model-based algorithm configuration. We selected the top 10 parameter combinations with high expected improvement scores and employed them into a numerical simulation model. The results show that a majority of the polarization curves obtained from the top combinations exceed the maximum power density of the original database. This method greatly saves the time of collecting fuel cell data for experiments and speeds up the parameter optimization process. |
| Author | Wang, Kaili Ban, Lishou Huang, Danyang Liu, Yanyi He, Jia Bian, Xihui Liu, Xijun Liu, Yifan Liu, Pengcheng |
| Author_xml | – sequence: 1 givenname: Lishou surname: Ban fullname: Ban, Lishou email: hejia1225@126.com organization: School of Chemistry and Chemical Engineering, Institute for New Energy Materials & Low-Carbon Technologies, School of Materials Science and Engineering, Tianjin University of Technology, Tianjin 300384, China. hejia1225@126.com – sequence: 2 givenname: Danyang surname: Huang fullname: Huang, Danyang email: hejia1225@126.com organization: School of Chemistry and Chemical Engineering, Institute for New Energy Materials & Low-Carbon Technologies, School of Materials Science and Engineering, Tianjin University of Technology, Tianjin 300384, China. hejia1225@126.com – sequence: 3 givenname: Yanyi surname: Liu fullname: Liu, Yanyi email: hejia1225@126.com organization: School of Chemistry and Chemical Engineering, Institute for New Energy Materials & Low-Carbon Technologies, School of Materials Science and Engineering, Tianjin University of Technology, Tianjin 300384, China. hejia1225@126.com – sequence: 4 givenname: Pengcheng surname: Liu fullname: Liu, Pengcheng email: hejia1225@126.com organization: School of Chemistry and Chemical Engineering, Institute for New Energy Materials & Low-Carbon Technologies, School of Materials Science and Engineering, Tianjin University of Technology, Tianjin 300384, China. hejia1225@126.com – sequence: 5 givenname: Xihui orcidid: 0000-0001-5554-7159 surname: Bian fullname: Bian, Xihui organization: State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China – sequence: 6 givenname: Kaili surname: Wang fullname: Wang, Kaili organization: School of Chemistry, Chemical Engineering and Environmental Engineering, Weifang University, Weifang 261061, China – sequence: 7 givenname: Yifan surname: Liu fullname: Liu, Yifan email: sparkle06@163.com organization: Suzhou Laboratory, Suzhou 215100, China. sparkle06@163.com – sequence: 8 givenname: Xijun orcidid: 0000-0002-2624-6901 surname: Liu fullname: Liu, Xijun email: xjliu@gxu.edu.cn organization: MOE Key Laboratory of New Processing Technology for Nonferrous Metals and Materials, Guangxi Key Laboratory of Processing for Non-ferrous Metals and Featured Materials, School of Resources, Environment and Materials, Guangxi University, Nanning, 530004 Guangxi, China. xjliu@gxu.edu.cn – sequence: 9 givenname: Jia surname: He fullname: He, Jia email: hejia1225@126.com organization: School of Chemistry and Chemical Engineering, Institute for New Energy Materials & Low-Carbon Technologies, School of Materials Science and Engineering, Tianjin University of Technology, Tianjin 300384, China. hejia1225@126.com |
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| Title | Electrode informatics accelerated the optimization of key catalyst layer parameters in direct methanol fuel cells |
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