Predicting Methane Dry Reforming Performance via Multi-Output Machine Learning: A Comparative Study of Regression Models

Abstract

Dry reforming of methane (DRM) offers a sustainable route to convert two major greenhouse gases—CH4 and CO2—into synthesis gas (syngas), enabling low-carbon hydrogen production and carbon utilization. This study applies fifteen machine learning (ML) regression models to simultaneously predict CH4 conversion, CO2 conversion, H2 yield, and CO yield using a published dataset of 27 experiments with Ni/CaFe2O4-catalyzed DRM. The comparative evaluation covers linear, tree-based, ensemble, and kernel-based algorithms under a unified multi-output learning framework. Feature importance analysis highlights reaction temperature, CH4/CO2 feed ratio, and Ni metal loading as the most influential variables. Predictions from the top-performing models (CatBoost and Random Forest) identify optimal performance windows—feed ratio near 1.0 and temperature between 780–820 °C—consistent with thermodynamic and kinetic expectations. Although no new catalysts are introduced, the study demonstrates how ML can extract actionable parametric insights from small experimental datasets, guiding future DRM experimentation and process optimization for hydrogen-rich syngas production.