Comparison of steam and dry reforming adsorption kinetics in solid oxide fuel cells

• A higher methane-to-oxidant ratio reduces overall methane conversion. • SRM demonstrates higher methane conversion than DRM. • Reaction rates increase with fuel cell temperature and current density. • Current density influences reforming kinetic parameters. • Proposed method’s R 2 value of ∼ 0.95 shows acceptable accuracy of results. Internal steam reforming (SRM) and dry reforming of methane (DRM) within solid oxide fuel cells offer significant potential for zero-carbon energy production. This study explores how electrochemical reactions influence reforming kinetics, which is crucial for designing fuel cell materials under various conditions. The research examines how gas composition, process temperature, and current draw from the fuel cell impact methane reforming adsorption kinetics. Both processes inside solid oxide fuel cells have been studied individually under varying conditions and anode materials, leaving a significant research gap in understanding electrochemical interactions’ impact on catalytic behavior within a unified fuel cell framework. Findings indicate that increasing methane-to- H 2 O and CO 2 ratio decreases methane conversion. Both processes show higher methane conversion with increased current density drawn from the fuel cell. In comparison, SRM achieves greater methane conversion than DRM under equal methane concentration in the feed stream. SRM also produces more hydrogen than DRM inside the fuel cell. Reforming reaction rates increase with fuel cell temperature, and DRM consumes methane slower than SRM. Higher methane concentration in the feed and current density boost reaction rates. The reaction order for H 2 O is generally higher than CO 2 in Langmuir–Hinshelwood model but lower than CH 4 . Both processes show reduced activation energy when current is drawn, with current density affecting H 2 O adsorption enthalpy more than CO 2 . The SRM model estimates activation energy more accurately, while the DRM model has an R 2 value close to 0.95, indicating acceptable accuracy.