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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,...

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Autores principales: Briceno-Mena, Luis A., Venugopalan, Gokul, Romagnoli, José A., Arges, Christopher G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
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.
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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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