Modeling and optimization of V2O5/TiO2 nanocatalysts for NH3-Selective catalytic reduction (SCR) of NOx by RSM and ANN techniques.

In the present study, two statistical methods including the response surface method (RSM) and artificial neural network (ANN), were employed for modeling and optimization of selective catalytic reduction of NOx with NH3 (NH3-SCR) over V2O5/TiO2 nanocatalysts. The relationship between catalyst preparation variables, such as metal loading, impregnation temperature, and calcination temperature on NO conversion were investigated. The R2 value of 0.9898 was obtained for quadratic a RSM model, which proves the high agreement of the model with the experimental data. The results of Pareto analysis revealed that three factors including calcination temperature, V loading, and impregnation temperature have a considerable impact on the response. Deduced from the established RSM model, the order of influence on the NO conversion was as follows: calcination followed by V loading and impregnation temperature. The optimum condition of catalyst preparation for maximum NO conversion over V2O5/TiO2 nanocatalysts was predicted to be at 0.0051 mol of V loading, an impregnation temperature of 50 °C and a calcination temperature of 491 °C. Moreover, an ANN model was created by a feed-forward back propagation network (with the topology 4, 12 and 1) to model the relation between the selected catalyst preparation variables and NH3-SCR process temperature. The R2 values for training, validation as well as test sets, were 0.99, 0.9810 and 0.9733. These high values proved the accuracy of the AAN model in modeling and estimating the NO conversion over V2O5/TiO2 nanocatalysts. According to the ANN model, the relative significance of each variable on NO conversion is calcination temperature, process temperature loading, and impregnation temperature from high to low importance, respectively, corroborating the obtained results from RSM.