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 (H₂) 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 °C and 80% at 800 °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.