Catalyst Breakthroughs in Methane Dry Reforming: Employing Machine Learning for Future Advancements

Rising levels of atmospheric carbon dioxide (CO₂) and methane (CH₄) have sparked the interest of researchers in resolving this issue. Various technologies have been utilized such as dry reformation of methane (DRM), that turns these greenhouse gases (GHGs) into syngas. Numerous studies have been done in recent years to produce efficient and competitive catalysts for DRM. However, a “state of the art” review is still required due to a lack of literature on improvements and scalability of robust catalytic systems. This study aims to evaluate recent advancements in catalyst formulations, their activities, stability, and selectivity. We have discussed a variety of materials, including Metal-Organic Frameworks (MOF), a type of two-dimensional transition metal carbides and nitrides (MXene), reducible and irreducible metal oxides, transition metal oxides, zeolites, carbon, and biochar-based catalysts, which are responsible for improving the effectiveness of the DRM process. Furthermore, machine learning (ML) approaches are highlighted for their potential in catalyst design and optimization, providing increased accuracy and efficiency. This review highlights major enhancements to the DRM method, which result in higher hydrogen yield and catalyst stability. Novel catalysts have been created to tackle crucial problems including coking and sintering, which are necessary to progress commercialization. To ensure successful commercial application, future research should concentrate on finding catalysts with excellent features that are also economically viable. This study provides a significant resource for future catalyst development and promotes greater cooperation between academic and commercial communities.