Abstract
<jats:p>Tri-reforming of methane (TRM) is a promising route for producing hydrogen-rich syngas while partially utilizing CO2, but its deployment is limited by trade-offs among methane conversion, syngas composition, reactor duty, and carbon deposition. In this study, a Gibbsequilibrium model in Aspen Plus was used to generate a database of 46,464 operating points spanning temperature, pressure, and inlet H2O/CH4, CO2/CH4, and O2/CH4 ratios. Decision tree, random forest, XGBoost, and artificial neural network models were trained and compared, followed by SHAP and permutation-importance analysis and multi-objective genetic-algorithm optimization. The neural network showed the best predictive performance. Explainability analysis identified temperature as the dominant factor governing carbon formation, followed by steam, carbon dioxide, and oxygen feed ratios. Optimization revealed multiple distinct operating windows, confirming that no single universal optimum exists and demonstrating a rapid, interpretable framework for low-emission TRM screening and design.</jats:p>