Dynamic NO_x emission prediction based on composite models adapt to different operating conditions of coal-fired...

An accurate NO_x concentration prediction model plays an important role in low NO_x emission control in power stations. Predicting NO_x in advance is of great significance in satisfying stringent environmental policies. This study aims to accurately predict the NO_x emission concentration at the outlet of boilers on different operating conditions to support the DeNO_x procedure. Through mutual information analysis, suitable features are selected to build models. Long short-term memory (LSTM) models are utilized to predict NO_x concentration at the boiler's outlet from selected input features and exhibit power in fitting multivariable coupling, nonlinear, and large time-delay systems. Moreover, a composite LSTM model composed of models on different operating conditions, like steady-state and transient-state condition, is prosed. Results of one whole day of typical operating data show that the accuracy of the NO_x concentration and fluctuation trend prediction based on this composite model is superior to that using a single LSTM model and other non-time-sequence models. The root mean square error (RMSE) and R² of the composite LSTM model are 3.53 mg/m³ and 0.89, respectively, which are better than those of a single LSTM (i.e., 5.50 mg/m³ and 0.78, respectively).