A hybrid approach for the dynamic monitoring and forecasting of NOx emissions in power plants

Abstract

The concentration of NOx emissions from thermal power plants affects the efficiency of selective catalytic reduction (SCR), and accurate NOx prediction is of great significance for environmental protection. Some literature proves that the prediction accuracy of NOx concentration can be improved by dividing the data into different operating conditions through algorithms or observation and training multiple models separately. However, this finding has some limitations in practice, for the model can’t automatically judge the working condition of the next stage before the data is generated. In order to fill this gap, this study proposed a novel NOx prediction method that first determines the operating conditions based on the power generation plan and then selects the appropriate forecasting model based on the predicted operating conditions. The corresponding model training data for different working conditions were established by referring to the suggestions provided by experts and the screening of mutual information. The system has been deployed at a coal-fired power plant in Yangzhou. Compared with the single model (the model initially deployed in the power plant), the proposed model is improved in four indicators (R2, MAPE, RMSE, and MSE) on the test dataset.