Recent developments and current trends on catalytic dry reforming of Methane: Hydrogen Production, thermodynamics analysis, techno feasibility, and machine learning

Dry reforming of methane (DRM) offers a promising pathway for sustainable fuel production by converting greenhouse gases, including CO2 and CH4, into valuable syngas (H2 and CO). However, the scalability of DRM technology remains a challenge due to the inherent stability of CO2 and CH4, necessitating the development of robust catalytic systems. While considerable efforts have been dedicated to investigating efficient DRM catalysts, there is an urgent need for a comprehensive review of the current state of research. This study aims to provide a comprehensive understanding of the intrinsic and extrinsic interactions among catalytic components, such as active metals and support materials, to enhance catalytic performance in DRM. The effectiveness of catalysts in DRM depends on various factors, including the selection of support materials, active phases, synthetic techniques, and reactor configurations. This investigation explores the impact of these factors on the catalytic performance and stability of specific catalysts. To achieve an economical catalyst with sustained activity and stability, this review examines the strategic utilization of synergistic interactions between noble, non-noble metals, and/or supports, leading to the development of catalysts. Additionally, the utilization of machine learning (ML) techniques, employing data-driven prediction models based on artificial intelligence, enables the modeling of DRM catalysts. ML technology offers benefits such as improved accuracy, time efficiency, and accelerated exploration of catalytic systems. Overall, this review serves as a fundamental resource for advancing catalyst design in DRM, facilitating effective communication and knowledge exchange within academic and commercial sectors.