Integrating advanced fitting models with experimental catalysis to maximize H2 production in dry reforming using nickel on metalized-silica-alumina catalysts

This study explores the enhancement of hydrogen production via dry reforming of methane (DRM) using nickel catalysts supported on metalized silica-alumina. By incorporating noble metals (Ir, Pd, Pt, Ru, and Rh), we significantly improve the catalysts' reducibility, basicity, and resistance to coke deposition. Our novel approach integrates a predictive model combining advanced statistical and experimental techniques to optimize catalyst performance. The active site population derived from the reduction of the NiAl2O4 phase is found to be stable and least affected under oxidizing gas stream (CO2) as well as reducible gas stream (H-2) during the DRM reaction. The catalyst system is characterized by surface area and porosity, X-ray diffraction, Raman spectroscopy, thermogravimetry analysis, XPS, TEM, and various temperature-programmed reduction/desorption techniques (TPR/CO2-TPD). Notably, the 5Ni/1IrSiAl catalyst shows reduced activity due to low reducibility and basicity, whereas the 5Ni/1RhSiAl catalyst demonstrates superior performance, achieving a hydrogen yield of 62% at 700 degrees C and 80% at 800 degrees C after 300 minutes. This enhancement is attributed to the highest edge of reducibility, the maximum concentration of stable active sites "Ni" (derived from NiAl2O4 during the DRM reaction), and the optimum concentration of moderate strength basic sites. Through the application of multiple response surface methodology and central composite design, we developed a predictive model that forecasts the optimal conditions for maximizing hydrogen yield, which was experimentally validated to achieve 95.4% hydrogen yield, closely aligning with the predicted 97.6%. This study not only provides insights into the mechanistic pathways facilitated by these catalysts but also demonstrates the efficacy of computational tools in optimizing catalytic performance for industrial applications.