A comprehensive analysis of kinetic modeling in dry reforming of methane

Abstract

Dry reforming of methane (DRM) is a key reaction for syngas production from greenhouse gases (methane and carbon dioxide). Reaction kinetics predict reaction rates, identify and understand reaction mechanisms, and guide catalyst and reactor design. To provide a clear sense of progress in DRM kinetic modeling, we conducted a comprehensive review and analysis of the approaches employed, their evolution, important model equations, and their applications. The modeling approaches are (i) empirical power law, (ii) mechanistic microkinetic, (iii) stochastic Monte Carlo, (iv) semi-empirical, and (v) AI-DFT hybrid frameworks. The analysis revealed that empirical and semi-empirical models primarily provide DRM activation energy for the catalyst. Microkinetic modeling has challenged traditional DRM understanding in notable ways. Kinetic Monte Carlo simulations explicitly resolve lateral interactions, diffusion, spatial inhomogeneity, and adsorbate correlations that mean-field microkinetics neglect, offering a mechanistically richer description of coverage-dependent chemistry. On the other hand, AI-DFT hybrid frameworks offer a data-efficient platform for screening catalyst compositions and architectures balancing methane turnover with carbon-oxidation rates. The review also presents limitations of these models and further strategies for overcoming them, insights beneficial to researchers in the field.