Response Characteristics Modeling of Efficiency and NO_x Emission for Power Station Boiler

In order to improve the efficiency and to reduce NOx emission in combustion, a mixed model of the coal-fired boiler was studied by using kernel principle component analysis (KPCA), e-support vector regression (e-SVR) and function-type model. In the process of modeling, principal components of the original inputs were extracted using KPCA for eliminating the nonlinear relationship and strong-coupling among original inputs, and then the 5-fold cross validation method was used to search the optimal free parameters of the model and select the optimal number of principle components. Finally, the mixed model was compared with back propagation neural-networks(BPNN) and e-SVR model respectively; it is shown that the mixed response characteristics model exceeds the other two models and has powerful prediction precision and generalization.