[TiO2],t and pH are named as the independent variables and the percentage of decolor(DC%)as the dependent variable.A Box-Behnken design and U12(6×4×3)design with three factors were established to form the training set and the validation set,respectively.And back propagation neural network(BPNN)model is adopted in order to establish the model of photo-catalytic degradation about weak acidic dyes mixture of the GRS big red and weak acid red R.The best result shows that the correlation coefficient(R)is 0.9345 and the mean relative error between the predictive value and experimental value(MRE(%))is 3.23,for training set;the value of R is 0.9257,MRE(%)is 2.75,for the validation set.Besides,we discussed the influences of the pH,[TiO2],and t vs.DC% by the BPNN model.The optimized experimental condition obtained is pH=5.0,[TiO2]=1.50g/L,and t=40min based on the BPNN model,and combination with optimization of nonlinear constraints programming.The experimental value of DC is 99.23%,the predictive value is 98.98%,and the relative error is-0.25% between the predictive value and the experimental value,in the optimized experimental condition.Above all these indicate that the model can not only simulate the system of photo-catalytic degradation commendably but also can obtain the optimal experimental conditions.
