A response surface methodology (RSM) involving a central composite design was applied to the modelling and optimization of a preparation of Mn/active carbon nanocatalysts in NH3-SCR of NO at 250 degrees C and the results were compared with the artificial neural network (ANN) predicted values. The catalyst preparation parameters, including metal loading (wt%), calcination temperature and pre-oxidization degree (v/v% HNO3) were selected as influence factors on catalyst efficiency. In the RSM model, the predicted values of NO conversion were found to be in good agreement with the experimental values. Pareto graphic analysis showed that all the chosen parameters and some of the interactions were effective on response. The optimization results showed that maximum NO conversion was achieved at the optimum conditions: 10.2 v/v% HNO3, 6.1 wt% Mn loading and calcination at 480 degrees C. The ANN model was developed by a feed-forward back propagation network with the topology 3, 8 and 1 and a Levenberg-Marquardt training algorithm. The mean square error for the ANN and RSM models were 0.339 and 1.176, respectively, and the R2 values were 0.991 and 0.972, respectively, indicating the superiority of ANN in capturing the nonlinear behaviour of the system and being accurate in estimating the values of the NO conversion.
Preparation of Cu/Activated Carbon (Cu/AC) catalyst was optimized for low temperature selective catalytic reduction of NO by using response surface methodology. A central composite design (CCD) was used to investigate the effects of three independent variables, namely pre-oxidization degree (HNO3%), Cu loading (wt.%) and calcination temperature on NO conversion efficiency. The CCD was consisted of 20 different preparation conditions of Cu/AC catalysts. The prepared catalysts were characterized by XRD and SEM techniques. Predicting NO conversion was carried out using a second order model obtained from designed experiments and statistical software Minitab 14. Regression and Pareto graphic analysis showed that all of the chosen parameters and some interactions were effective on the NO conversion. The optimal values were pre-oxidization in 10.2% HNO3, 6.1 wt.% Cu loading and 480°C for calcination temperature. Under the optimum condition, NO conversion (94.3%) was in a good agreement with predicted value (96.12%).
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