Multiscale modeling of catalyst deactivation in dry methane reforming

Dry methane reforming (DRM) presents a promising strategy to convert greenhouse gases (CH4 and CO2) into syngas, a valuable precursor for the production of long-chain alkanes. However, catalyst deactivation remains a major issue, primarily caused by the deep cracking of CH4 and CO2 as well as the metal-catalyzed growth of carbon whiskers. To address this challenge, we introduce a multiscale model that analyzes DRM reactions on the Ni surface and predicts whisker formation. The multiscale modeling approach integrates microscopic kinetic Monte Carlo (kMC) simulations for surface reactions, mesoscopic pellet-scale modeling for predicting whisker growth length and macroscopic modeling to consider packed bed porosity and pressure drop across the reactor. Further, by systematically introducing time-steppers using the gap-tooth scheme, we significantly improved the computational efficiency, reducing the computational time from 17 days to 6 h at the expense of minimal accuracy loss in predicting whisker length. Based on this, we assert that such a multiscale model enables the analysis of various operating conditions affecting catalyst deactivation and kinetics of surface reaction, aiding in the design and optimization of DRM process.