Parametric study of the decomposition of methane for CO_x-free H₂ and high valued carbon using Ni-based catalyst via machine-learning simulation

Abstract

With industrial informatization, abundant data provides solutions for the digital design of methane-based hydrogen production. Catalytic methane decomposition (CMD) is a promising strategy for COx-free hydrogen production, with high-value carbon products generated. However, affected by various factors, the proper process parameters are challenge to be ascertained by the time-consuming experimental method. In this study, five machine learning methods were utilized for the precise prediction of methane conversion using Ni-based catalysts. Combined with SHAP method and univariate analysis method, XGBoost model with the best accuracy (with R² = 0.894, RSME = 7.724) was selected for the exploration of the reaction impact of active phase loading, support loading, and reaction conditions in methane convention, hydrogen production, carbon yield, and carbon quality. The result shows that methane conversion rate is mainly influenced by space velocity, reaction temperature, nickel loading, and methane percentage. Copper doping significantly affects carbon yield and its quality, and there is a strong bond between Ni and Al₂O₃, contributing the most to the reaction. This work would provide a guidance for the efficient catalyst design and effective hydrogen production.

Keywords

Catalytic methane decomposition / Machine learning simulation / Catalyst / Carbon / Hydrogen

CRediT authorship contribution statement

Dinghao Xue: Writing - original draft, Data curation. Pingyang Zhang: Writing - review & editing, Methodology. Yuanyuan Lin: Methodology, Data curation. Wenshuo Wang: Methodology, Data curation. Jiachang Shi: Methodology, Data curation. Qiang Hu: Writing - review & editing. Gartzen Lopez: Writing - review & editing, Investigation. Cristina Moliner: Writing - review & editing, Investigation. Jin Sun: Methodology, Investigation. Tao Wang: Methodology, Investigation. Xinyan Zhang: Methodology, Investigation. Yingping Pang: Methodology, Investigation. Xiqiang Zhao: Methodology, Investigation. Yanpeng Mao: Methodology, Investigation. Zhanlong Song: Writing - review & editing, Methodology, Investigation. Ziliang Wang: Supervision, Funding acquisition, Conceptualization. Wenlong Wang: Writing - review & editing, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was generously supported by Shandong Province Excellent Youth Science Fund Project (2023HWYQ-022), Taishan Scholars Youth Expert Program of Shandong Province (tsqn202312002), National Natural Science Foundation of China (52476207), and Qilu Youth Scholar Program of Shandong University.

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