Investigation Of Preparation And Intrinsic Kinetics Of Silicon Carbide Monolithic Catalysts For The Catalytic Combustion Of Lean Methane

The preparation and performance evaluation of Cu-Zn-Mn/SiC monolithic catalyst were carried out taking the catalytic combustion of lean methane as model reaction. The intrinsic kinetic experiments over Cu-Zn-Mn/SiC catalyst selected here for methane catalytic combustion were also fulfilled, and the intrinsic kinetic models were developed in which were in accord with the experimental data.Based on the impregnation and heat adsorption loading method of metal-oxide catalyst preparation, a series of Cu-Zn-Mn/SiC catalyst for lean methane catalytic combustion were prepared. And the influence of Cu/Zn/Mn proportions, active component’s loading method, active component’s loading content, etc. on the performance of the catalysts were investigated combining the performance evaluation and SEM、TPR、 XPS characterization technology. The results showed that, wetness impregnation method is better than heat adsorption method for active component loading, the appropriate loading content of active component is12%. However, heat adsorption method is better than wetness impregnation method for adding addictive, the appropriate adding content of addictive is2%. The SiC carbide monolithic catalyst with the active component Cu/Zn/Mn proportion as2:1:1and its loading content as12%, the loading content of addictive ZrO2as2%has the best performance on lean methane’s catalytic combustion. Its characteristic temperature T50and T80are502℃and700℃, respectively.The intrinsic kinetic experiments of the catalytic combustion of lean methane on Cu-Zn-Mn/SiC catalyst were carried out in a micro isothermal integral fixed bed reactor (Φ10×2mm、L300mm) under the conditions of pressure as0.1MPa, temperature range from500to700℃and methane molar fraction of feedstock from0.1%to1%. Two kinds of intrinsic kinetic models of Cu-Zn-Mn/SiC catalyst, which are hyperbolic equation and power-law, respectively, were developed, and the model parameters were estimated by Simplex optimization method and Runge-Kutta method. Statistical tests and the residual distribution result showed that these models were all highly acceptable and reliable.