Prediction of NO_x emission from power plant boiler based on mixed deep learning network
WU Song, MA Yongguang
Department of Automation, North China Electric Power University, Baoding 071003, China
Abstract: To reduce nitrogen oxide (NO_x) emissions from coal-fired power plant boiler, optimizing combustion and denitration control, a hybrid deep learning network model based on data-driven fusion of convolutional neural network (CNN) and bidirectional long short memory network (BiLSTM) is proposed, and the network hidden layer output global attention mechanism (GAM) is introduced to predict NO_x emissions from boileroutlet. Firstly, the big data of boiler operation history of a 2×300 MW power station is extracted and preprocessed. Then, on the basis of determining the input variables of the model, the time delay between each input variable and NO_x emission is calibrated by mutual information (MI) theory, and the sample sequence is reconstructed. Finally, the CNN-BiLSTM-GAM model is constructed and the prediction effect of this model is compared with several other typical prediction models. Experimental results show that the root mean square error (RMSE), mean absolute percentage error (MAPE) and determination coefficient (R-Square) of the model are 1.866, 0.44% and 0.983, which are the best among all models, indicating that this model has higher prediction accuracy and better generalization ability than other models.
Keywords: power plant boiler;NO_x emission prediction;deep learning;convolutional neural network;bidirectional long short memory network;global attention mechanism;mutual information
2022, 48(10):166-174  收稿日期: 2022-03-31;收到修改稿日期: 2022-07-27
基金项目: 河北省科技厅重点研发计划（18214523）
作者简介: 武松（1990-），男，河北石家庄市人，硕士研究生，专业方向为发电系统建模、仿真与优化控制
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