Abstract
NOx concentration is an important indicator of the response to ammonia dosage and nitrogen emissions, and its accurate prediction allows for efficient and rational optimal control of ammonia dosage. Due to the large external noise, time lag and non-linearity of the cement denitrification process, it is difficult to derive accurate mathematical prediction models. Therefore, a new machine learning model, namely EEMD-MImRMR-BASBP, is developed. Firstly, Ensemble Empirical Mode Decomposition (EEMD) and median-averaged filtering is used to process the data and remove the noise. In order to handle the large time lags, non-linearity and non-smoothness among the variables, mutual information (MI) based on the entropy principle is proposed to calculate the lag time of the non-linear system; furthermore, according to the feature variable selection method of Max-Relevance and Min-Redundancy (mRMR), the factors with strong influence are selected as the input variables of the prediction model in combination with the results of the mechanism analysis. Then, the EEMD-MImRMR-BASBP model to predict NOX concentration is constructed, in which the initialization parameters of the Back Propagation Neural Network (BP) are searched by Beetle Antennae Search (BAS) to effectively overcome the parameter selection problem of traditional BP prediction models. Finally, the model was applied for the NOX concentration prediction of a real cement plant in Jiang xi and Fu ping and compared with the classical BP-based prediction model, BASBP model, the root means square error (RMSE) and mean absolute error (MAE) of the EEMD-MImRMR-BASBP model for the two production lines are only 0.2927, 0.3513 and 0.1795, and 0.2383, which have better prediction performance compared with the current model.