To realize prediction of denitration efficiency and activity of the catalyst under stationary test condition by using geometric and physicochemical characteristics of the flue gas denitration catalyst

the correlation between the process characteristics and geometric and physical/chemical properties of selective catalytic reduction(SCR) denitrification catalyst was analyzed. The correlation coefficient matrix analysis and principal component analysis(PCA) were adopted to simplify the input parameters

and the improved neural network predictive model was proposed. Through training of a large amount of samples

the prediction model of denitration efficiency and activity for SCR denitration catalyst was established and then used to carry out prediction on the basis of the actual measurement data. The results indicate that

the PCA-LMBP has a high accuracy

which has great guide meaning and application value on the performance test and quality monitoring works for SCR denitration catalysts.