Based on the experimental data of Mn1Co0.5Cr0.5Ox catalysts and the component transport model in Computational Fluid Dynamics (CFD), a kinetic model for the standard NH3-SCR (NH3 selective catalytic reduction) process was effectively established. The objective of the model development was to predict the denitrification reaction rate of the catalyst, which incorporates various factors such as the Arrhenius parameters (Pre-exponential factor and activation energy), inertial resistance, viscous resistance, and surface-to-volume ratio. To verify the practicability of the model, simulation results were compared with actual experimental data. The effects of NH3, NO, O2 concentrations, and gas hourly space velocity (GHSV) on NO conversion were simulated and analyzed. Subsequently, the NO conversion prediction model was trained and established using a combination of numerical simulation results, back-propagation neural network, and genetic algorithm (BP-GA). Furthermore, the significance of the impact that various factors had on the denitrification activity of the catalyst was determined.
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