Interpretable SHAP Analysis of Key Operating Parameters in Methane Dry Reforming

Dry reforming of methane (DRM) is a key reaction for syngas production and greenhouse gas utilisation, involving multiple interacting operating variables. In this work, an interpretable machine learning approach based on CatBoost regression coupled with SHapley Additive exPlanations (SHAP) is applied to a previously published DRM dataset to analyse the influence of reaction temperature, CH4/CO2 feed ratio, and Ni loading on CH4 and CO2 conversions and H2 and CO yields. The objective of this study is methodological rather than experimental, focusing on the use of interpretable machine learning to extract variable importance hierarchies and conditional interaction effects from data-limited DRM studies. The analysis confirms that reaction temperature is the dominant controlling parameter, while feed ratio and Ni loading exhibit secondary, regime-dependent influences. No new catalytic mechanisms or experimental findings are proposed. The results illustrate how CatBoost–SHAP analysis can complement experimental DRM research by providing transparent, quantitative interpretation of published datasets under realistic data constraints. These findings are consistent with established DRM thermodynamic and kinetic behaviour, where temperature governs endothermic reforming reactions, while feed composition and metal loading influence carbon formation pathways and catalytic activity.