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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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Published: |
Elsevier
2024
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/45141/ https://doi.org/10.1016/j.ijhydene.2024.05.413 |
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| Summary: | 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. |
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