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Machine learning for guiding high-temperature PEM fuel cells with greater power density
High-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) are enticing energy conversion technologies because they use low-cost hydrogen generated from methane and have simple water and heat management. However, proliferation of this technology requires improvement in power density. Here,...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892359/ https://www.ncbi.nlm.nih.gov/pubmed/33659908 http://dx.doi.org/10.1016/j.patter.2020.100187 |
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author | Briceno-Mena, Luis A. Venugopalan, Gokul Romagnoli, José A. Arges, Christopher G. |
author_facet | Briceno-Mena, Luis A. Venugopalan, Gokul Romagnoli, José A. Arges, Christopher G. |
author_sort | Briceno-Mena, Luis A. |
collection | PubMed |
description | High-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) are enticing energy conversion technologies because they use low-cost hydrogen generated from methane and have simple water and heat management. However, proliferation of this technology requires improvement in power density. Here, we show that Machine Learning (ML) tools can help guide activities for improving HT-PEMFC power density because these tools quickly and efficiently explore large search spaces. The ML scheme relied on a 0-D, semi-empirical model of HT-PEMFC polarization behavior and a data analysis framework. Existing datasets underwent support vector regression analysis using a radial basis function kernel. In addition, the 0-D, semi-empirical HT-PEMFC model was substantiated by polarization data, and synthetic data generated from this model was subject to dimension reduction and density-based clustering. From these analyses, pathways were revealed to surpass 1 W cm(−2) in HT-PEMFCs with oxygen as the oxidant and CO containing hydrogen. |
format | Online Article Text |
id | pubmed-7892359 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-78923592021-03-02 Machine learning for guiding high-temperature PEM fuel cells with greater power density Briceno-Mena, Luis A. Venugopalan, Gokul Romagnoli, José A. Arges, Christopher G. Patterns (N Y) Article High-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) are enticing energy conversion technologies because they use low-cost hydrogen generated from methane and have simple water and heat management. However, proliferation of this technology requires improvement in power density. Here, we show that Machine Learning (ML) tools can help guide activities for improving HT-PEMFC power density because these tools quickly and efficiently explore large search spaces. The ML scheme relied on a 0-D, semi-empirical model of HT-PEMFC polarization behavior and a data analysis framework. Existing datasets underwent support vector regression analysis using a radial basis function kernel. In addition, the 0-D, semi-empirical HT-PEMFC model was substantiated by polarization data, and synthetic data generated from this model was subject to dimension reduction and density-based clustering. From these analyses, pathways were revealed to surpass 1 W cm(−2) in HT-PEMFCs with oxygen as the oxidant and CO containing hydrogen. Elsevier 2021-01-08 /pmc/articles/PMC7892359/ /pubmed/33659908 http://dx.doi.org/10.1016/j.patter.2020.100187 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Briceno-Mena, Luis A. Venugopalan, Gokul Romagnoli, José A. Arges, Christopher G. Machine learning for guiding high-temperature PEM fuel cells with greater power density |
title | Machine learning for guiding high-temperature PEM fuel cells with greater power density |
title_full | Machine learning for guiding high-temperature PEM fuel cells with greater power density |
title_fullStr | Machine learning for guiding high-temperature PEM fuel cells with greater power density |
title_full_unstemmed | Machine learning for guiding high-temperature PEM fuel cells with greater power density |
title_short | Machine learning for guiding high-temperature PEM fuel cells with greater power density |
title_sort | machine learning for guiding high-temperature pem fuel cells with greater power density |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892359/ https://www.ncbi.nlm.nih.gov/pubmed/33659908 http://dx.doi.org/10.1016/j.patter.2020.100187 |
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