Dynamic Prediction for Pollutant Emissions of Coal-fired Power Plant Based on CNN-LSTM-Attention

Abstract

Abstract The prediction accuracy of pollutant emissions by using traditional modeling methods is unsatisfactory in dynamic conditions. To overcome the problem, data-driven modeling was introduced to build the dynamic model of pollutant emissions of power plants in this paper. Combining with the running data of a 300MW circulating fluidized bed (CFB) unit, the dynamic prediction models of SO2 and NO x emissions were established respectively by using conventional neural network-long short-term memory and attention mechanism (CNN-LSTM-Attention). Moreover, LSSVM, LSTM and CNN-LSTM were introduced for comparison to demonstrate the superiority of CNN-LSTM-Attention model respectively. Simulation results indicate that model can imitate change trend of actual data with high accuracy over a long period of time. Compared with LSSVM, LSTM and CNN-LSTM, the proposed model has better modeling performance under different load conditions. This work provides certain guidance for the application of deep learning in the industrial field.