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Exploring the role of process control and catalyst design in methane catalytic decomposition: A machine learning perspective

Methane catalytic pyrolysis presents a promising avenue for producing CO2-free hydrogen. However, optimizing catalyst composition and process parameters through experimental studies has been resource-intensive. Machine learning techniques have been increasingly utilized to deepen the understanding o...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Wen, Yuming, Wang, Shule, Wu, Libo, Hondo, Emmerson, Tang, Chuchu, Jiang, Jianchun, Ho, Ghim W., Kawi, Sibudjing, Wang, Chi-Hwa
التنسيق: مقال
منشور في: Elsevier 2024
الموضوعات:
QD Chemistry
الوصول للمادة أونلاين:http://eprints.um.edu.my/45141/
https://doi.org/10.1016/j.ijhydene.2024.05.413
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id my.um.eprints.45141
record_format eprints
spelling my.um.eprints.451412024-09-19T07:41:33Z http://eprints.um.edu.my/45141/ Exploring the role of process control and catalyst design in methane catalytic decomposition: A machine learning perspective Wen, Yuming Wang, Shule Wu, Libo Hondo, Emmerson Tang, Chuchu Jiang, Jianchun Ho, Ghim W. Kawi, Sibudjing Wang, Chi-Hwa QD Chemistry Methane catalytic pyrolysis presents a promising avenue for producing CO2-free hydrogen. However, optimizing catalyst composition and process parameters through experimental studies has been resource-intensive. Machine learning techniques have been increasingly utilized to deepen the understanding of process mechanisms. In this study, machine learning models were developed to deepen the understanding of the effects of process control and catalyst design on CH4 conversion performance. The optimal CH4 conversion model achieved a testing R2 of 0.9999. To further assess the model's real-world applicability, an initial verification experiment was conducted using a catalyst composition and process parameters distinct from those in the dataset. The results reaffirmed the model's good accuracy in real-world scenarios. Delving deeper into the intricacies of methane pyrolysis, the Shapley values and partial dependence plots generated by the model were scrutinized and discussed to provide a new perspective on the influence of process control and catalyst design parameters on methane catalytic decomposition. Elsevier 2024-06 Article PeerReviewed Wen, Yuming and Wang, Shule and Wu, Libo and Hondo, Emmerson and Tang, Chuchu and Jiang, Jianchun and Ho, Ghim W. and Kawi, Sibudjing and Wang, Chi-Hwa (2024) Exploring the role of process control and catalyst design in methane catalytic decomposition: A machine learning perspective. International Journal of Hydrogen Energy, 72. pp. 601-613. ISSN 0360-3199, DOI https://doi.org/10.1016/j.ijhydene.2024.05.413 <https://doi.org/10.1016/j.ijhydene.2024.05.413>. https://doi.org/10.1016/j.ijhydene.2024.05.413 10.1016/j.ijhydene.2024.05.413
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QD Chemistry
spellingShingle QD Chemistry
Wen, Yuming
Wang, Shule
Wu, Libo
Hondo, Emmerson
Tang, Chuchu
Jiang, Jianchun
Ho, Ghim W.
Kawi, Sibudjing
Wang, Chi-Hwa
Exploring the role of process control and catalyst design in methane catalytic decomposition: A machine learning perspective
description Methane catalytic pyrolysis presents a promising avenue for producing CO2-free hydrogen. However, optimizing catalyst composition and process parameters through experimental studies has been resource-intensive. Machine learning techniques have been increasingly utilized to deepen the understanding of process mechanisms. In this study, machine learning models were developed to deepen the understanding of the effects of process control and catalyst design on CH4 conversion performance. The optimal CH4 conversion model achieved a testing R2 of 0.9999. To further assess the model's real-world applicability, an initial verification experiment was conducted using a catalyst composition and process parameters distinct from those in the dataset. The results reaffirmed the model's good accuracy in real-world scenarios. Delving deeper into the intricacies of methane pyrolysis, the Shapley values and partial dependence plots generated by the model were scrutinized and discussed to provide a new perspective on the influence of process control and catalyst design parameters on methane catalytic decomposition.
format Article
author Wen, Yuming
Wang, Shule
Wu, Libo
Hondo, Emmerson
Tang, Chuchu
Jiang, Jianchun
Ho, Ghim W.
Kawi, Sibudjing
Wang, Chi-Hwa
author_facet Wen, Yuming
Wang, Shule
Wu, Libo
Hondo, Emmerson
Tang, Chuchu
Jiang, Jianchun
Ho, Ghim W.
Kawi, Sibudjing
Wang, Chi-Hwa
author_sort Wen, Yuming
title Exploring the role of process control and catalyst design in methane catalytic decomposition: A machine learning perspective
title_short Exploring the role of process control and catalyst design in methane catalytic decomposition: A machine learning perspective
title_full Exploring the role of process control and catalyst design in methane catalytic decomposition: A machine learning perspective
title_fullStr Exploring the role of process control and catalyst design in methane catalytic decomposition: A machine learning perspective
title_full_unstemmed Exploring the role of process control and catalyst design in methane catalytic decomposition: A machine learning perspective
title_sort exploring the role of process control and catalyst design in methane catalytic decomposition: a machine learning perspective
publisher Elsevier
publishDate 2024
url http://eprints.um.edu.my/45141/
https://doi.org/10.1016/j.ijhydene.2024.05.413
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score 13.251813

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