Against the non-linear

time-varying and time-delayed characteristics of selective catalytic reduction(SCR) denitrification system

this paper established a predictive model of the SCR denitrification system based on wavelet denoising

principal component analysis and adaptive neuro-fuzzy inference system(ANFIS). By analyzing the results of different threshold selection principles

wavelet bases and decomposition levels

the most suitable denoising method for the system data signals was selected

and the principal component analysis method was adopted for data dimensionality reduction. Then

the initial network structure of the ANFIS model was constructed based on subtractive clustering

and the model parameters were optimized by a hybrid algorithm. Finally

the model was verified by the operational data of a coal-fired power plant

and the results were compared with the BP neural network model. The results showed that the ANFIS denitrification system model based on wavelet denoising and principal component analysis has higher fitting accuracy and better generalization ability.