Spatially-resolved investigation of CO2 methanation over Ni/γ-Al2O3 and Ni3.2Fe/γ-Al2O3 catalysts in a packed-bed reactor.

[Display omitted] • CO 2 methanation over Ni-based catalysts studied with spatially-resolved experiments and modeling. • Strong gradients along catalyst bed cause oxidation and structural changes. • Model approach incorporates active catalytic surface area variation based on operando XAS data. • Combination of microkinetic modeling and operando experiments leads to better understanding of chemical kinetics. CO 2 methanation via the Sabatier reaction with (green) H 2 is promising due to its role in achieving a carbon–neutral energy balance in the context of Power-to-Gas technologies. Since Ni-based catalysts are relatively inexpensive compared to other metals and exhibit high catalytic activity, they are most commonly used. Due to the exothermic nature of the reaction, strong temperature and concentration gradients occur, which influence the catalyst structure. Thus, revealing the effects of structural changes of the catalyst along the reactor bed on local activity and selectivity is essential. A 1D packed-bed reactor model was used for numerical simulations, coupled with detailed microkinetics and mass transport limitations. The simulation results are compared with axially-resolved concentration and temperature profiles over 17 wt% Ni/γ-Al 2 O 3 and 17 wt% Ni 3.2 Fe/γ-Al 2 O 3 catalysts at oven temperatures of 623 K and 723 K. Using additional information from structural spatially-resolved synchrotron-based operando X-ray absorption spectroscopy studies, the oxidation state of Ni was considered in modeling the reactor by changing the catalytically active surface area along the reactor. Predicted surface coverages are compared with surface species experimentally determined by diffuse reflectance infrared Fourier transform spectroscopy. Overall, this study demonstrates the importance of combining modeling with spatially-resolved and temperature-dependent experiments to improve multiscale models and make predictions more accurate. [ABSTRACT FROM AUTHOR]