Dynamic NOX emission concentration prediction based on the combined feature selection algorithm and deep neural network

Abstract

The development of an accurate nitrogen oxide (NOx) prediction model is difficult because of multiple parameters, strong coupling, and long delay time of selective catalytic reduction (SCR) systems. In this study, a modeling scheme based on combined feature selection, the JAYA optimization algorithm, and deep neural network (DNN) was developed. First, historical operating data preprocessing, including eliminating outlier points and normalization, was completed. Next, a combined feature selection algorithm based on classification and regression tree, random forest, extreme gradient boosting, and maximal information coefficient (MIC) was developed to select critical input variables. Subsequently, the delay time of the input variables was calculated based on the MIC and JAYA algorithm, and the modeling data were reconstructed. Finally, the real-time dynamic prediction of the SCR outlet NOx concentration was realized based on the DNN model. Experimental results based on operation data of 1000 MW ultra-supercritical boiler revealed that the prediction errors of the established models were less than 5%. Thus, could accurately predict the NOx emission concentration at the outlet of SCR system.