热力发电2025，Vol.54Issue(7)：33-42,10.DOI:10.19666/j.rlfd.202408194
基于CNN-GRU-MHA的CFB机组污染物排放动态预测
Dynamic prediction of pollutants emission from circulating fluidized bed unit based on CNN-GRU-MHA
摘要
Abstract
The accurate prediction of SO2 and NOx emission mass concentrations can effectively guide the control of pollutants emissions,which is of great significance for the environmental protection operation of circulating fluidized bed(CFB)units.A 330 MW CFB unit is taken as the research object,and the Pearson coefficient is used to realize the screening of input variables,and the interquartile range(IQR)method is applied to screen the outliers and replace them with the normalization at the same time,to complete the data preprocessing.Subsequently,the features of input variables are extracted by convolutional neural network(CNN),and by entering into the gate-recurrent unit(GRU)the time-series features are processed.The multi-head self-attention(MHA)mechanism is introduced to capture the important relationships between features,and the model output is obtained after training.Finally,the results of the test set are evaluated using the mean absolute error(MAE),mean absolute percentage error(MAPE),and the coefficient of determination(R2).The results show that the model is able to predict the pollutants mass concentration in CFBs more accurately and achieve good prediction results,and the superior performance of the model is proved by the comparison of ablation experiments with the model.The proposed CNN-GRU-MHA model can realize the monitoring and optimization guidance of pollutants emissions CFB units,so that the power plant can adjust the operation parameters in time to ensure that the pollutants emissions meet the standards.关键词
CFB/污染物排放预测/深度学习/数据驱动Key words
circulating fluidized bed/pollutant emission prediction/deep learning/data-driven引用本文复制引用
王勇权,高明明,王唯铧,张鹏新,成永强..基于CNN-GRU-MHA的CFB机组污染物排放动态预测[J].热力发电,2025,54(7):33-42,10.基金项目
国家重点研发计划项目(2022YFB4100304) National Key Research and Development Program(2022YFB4100304) （2022YFB4100304）
