Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO2 Methanation Catalysts with High Activity at Low Temperatures

Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO2 Methanation Catalysts with High Activity at Low Temperatures

CO2 methanation represents a promising technological pathway for achieving efficient carbon dioxide resource utilization and mitigation of greenhouse gas emissions. However, the development of CO2 methanation catalysts with high activity at low temperatures (

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Veröffentlicht in:Industrial & engineering chemistry research 2024-08, Vol.63 (33), p.14727-14747
Hauptverfasser: Yang, Qingchun, Bao, Runjie, Rong, Dongwen, Xiao, Jingxuan, Zhou, Jianlong, Zhao, Lei, Zhang, Dawei
Format: Artikel
Sprache:eng
Schlagworte:
Process Systems Engineering
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Zusammenfassung:CO2 methanation represents a promising technological pathway for achieving efficient carbon dioxide resource utilization and mitigation of greenhouse gas emissions. However, the development of CO2 methanation catalysts with high activity at low temperatures (
ISSN:0888-5885
1520-5045
DOI:10.1021/acs.iecr.4c01708