Prediction of SO2, NOx and PM in the sintering process based on deep learning

Abstract

The accurate prediction of SO2, NOx and PM emissions in the iron ore sintering process could adjust the desulfurization and denitrification operation in time. The study presented an integrated prediction model for SO2, NOx and PM in sintering flue gas. Gradient boosting decision tree, recurrent neural network, gated recurrent unit were chosen as sub-models to predict SO2, NOx and PM by comparing different regression prediction models, which were then combined to form an integrated prediction model (MMEP). The box plots, empirical mode decomposition algorithm, Pearson correlation coefficient and maximum information coefficient to select independent variables for the predictive model. The MMEP model had an overall accuracy greater than 0.82, as verified by production data, which could provide guidance for on-site sintering production.