Prediction of <scp> SO ₂ </scp> concentration in <scp>WFGD</scp> system based on <scp>GWO</scp> optimized <scp>CNN</scp> ‐ <scp>BiLSTM</scp> ‐attention

Abstract Due to external disturbances, the parameters of the desulphurization system are uncertain, and their relationships are complex, which makes it difficult to predict the concentration of SO 2 at the desulphurization system outlet. In this paper, grey wolf optimization (GWO) optimized convolutional neural network (CNN)‐bi‐directional long short‐term memory (BiLSTM)‐Attention algorithm was used for prediction, and the problem of low SO 2 concentration prediction accuracy at outlet has been resolved. First, the outliers of the thermal power plant desulphurization data were processed using the local outlier factor (LOF) algorithm. Secondly, CNN‐BiLSTM model was constructed using CNN and BiLSTM, and attention module was added to realize feature extraction and better capture the regularity of input data. Then, the CNN‐BiLSTM‐Attention model was optimized using GWO and its hyperparameters were improved. Finally, based on the Matlab R2023a platform, the prediction comparison as well as the error analysis of the desulphurization data were carried out. In the prediction of SO 2 concentration in low‐flow continuous slurry supply mode, the error of the combined model decreased by 23.2% on average compared to the CNN‐BiLSTM‐Attention model. In the prediction of SO 2 concentration in the high‐flow intermittent slurry supply mode, the error of the combined model decreased by 20.8% on average. According to the results, the combined model surpasses both the single model and several other algorithmic combination models in terms of performance metrics, and the predictions are more accurate.