Machine learning applications in catalytic hydrogenation of carbon dioxide to methanol: A comprehensive review.

The catalytic hydrogenation of carbon dioxide (CO 2) to methanol presents a significant opportunity for both mitigating climate change and producing a valuable chemical feedstock. While existing reviews delve into diverse modeling strategies, the role and potential of machine learning (ML) approaches remain largely unexplored. This review addresses the gap by comprehensively exploring the mechanism, workflow, and application of ML models in the methanol production process. The review highlights the significance of ML application in catalytic CO 2 hydrogenation for methanol synthesis, emphasizing process optimization, predicting methanol performance indicators, thermodynamic modeling, reaction kinetics, and assessing catalyst activity. Furthermore, the review delves into cutting-edge approaches like hybrid models, gray-box models, and digital twins, showcasing their potential to revolutionize the methanol production process. This comprehensive review serves as a valuable resource for forthcoming research aimed at optimizing the CO 2 conversion process to efficiently and sustainably produce methanol. [Display omitted] • Machine learning models are extensively employed in catalytic hydrogenation to produce methanol. • This review is dedicated to the analysis and synthesis of these models as well as to their applications. • ML exhibits high accuracy in modeling CO 2 hydrogenation into methanol. • ML models predict key performance indicators of methanol and optimize reaction conditions. • Innovative ML, gray box, digital twin, and Hybrid algorithms leverage vast databases in this domain. [ABSTRACT FROM AUTHOR]