Dynamic NOx emission prediction based on composite models adapt to different operating conditions of coal-fired utility boilers

An accurate NOx concentration prediction model plays an important role in low NOx emission control in power stations. Predicting NOx in advance is of great significance in satisfying stringent environmental policies. This study aims to accurately predict the NOx 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 NOx 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 NOx 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 R-2 of the composite LSTM model are 3.53 mg/m(3) and 0.89, respectively, which are better than those of a single LSTM (i.e., 5.50 mg/m(3) and 0.78, respectively).

Automated summary

An accurate NOx concentration prediction model is crucial for low NOx emission control in power stations to comply with environmental policies. This study utilizes LSTM models to predict NOx concentrations at different operating conditions, proposing a composite model for improved accuracy. Results show that the composite LSTM model outperforms single LSTM and other non-time-sequence models in predicting NOx concentration and fluctuation trends.