The NO_x produced in the combustion process of coal in thermal power plants is one of the most important sources of air pollutants. An effective prediction model should be established for reducing the NO_x emissions. In view of the massive and high dimensional system data of thermal power plants and the multivariable mutual coupling characteristics of the coal-fired boilers

the components analysis method was applied to extract the data from the DCS and eliminate the coupling of each feature variable. Then

the extracted data were used as input of the long short term memory(LSTM) neural network to establish the prediction model for NO_x emission. Moreover

this model

the conventional circulating neural network(RNN) model of NO_x emission prediction and the least square support vector machine(LSSVM) model were applied to the actual working condition data of an ultra supercritical 660 MW coal-fired boiler. The results show that

the prediction effect of the LSTM and RNN models was better than that of the LSSVM model. The prediction accuracy of the proposed model was 79% and the rootmean-square error was 0.398

indicating the proposed LSTM model is superior to the conventional RNN model and the LSSVM model. Furthermore

the data tracking effect of the model is obviously better than that of the RNN model

the prediction result fluctuates less and the model has higher stability and accuracy.