A dynamic prediction model of SO<sub>2</sub> concentration at the exit of desulfurization system based on TCN-BiLSTM

WANG Feng; ZHOU Ti; YU Weizhen; ZOU Peng; LI Boyi; LIU Shaojie; ZHAO Zheng

doi:10.13205/j.hjgc.202609012

Volume 44 Issue 9

Sep. 2026

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2026 > 44(9): 108-116

WANG Feng, ZHOU Ti, YU Weizhen, ZOU Peng, LI Boyi, LIU Shaojie, ZHAO Zheng. A dynamic prediction model of SO2 concentration at the exit of desulfurization system based on TCN-BiLSTM[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(9): 108-116. doi: 10.13205/j.hjgc.202609012

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WANG Feng, ZHOU Ti, YU Weizhen, ZOU Peng, LI Boyi, LIU Shaojie, ZHAO Zheng. A dynamic prediction model of SO₂ concentration at the exit of desulfurization system based on TCN-BiLSTM[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(9): 108-116. doi: 10.13205/j.hjgc.202609012

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A dynamic prediction model of SO₂ concentration at the exit of desulfurization system based on TCN-BiLSTM

doi: 10.13205/j.hjgc.202609012

1. China Power Huachuang (Suzhou) Electric Power Technology Research Co., Ltd, Suzhou 215000, China;
2. Shanxi Zhongdian Shentou Second Power Generation Co., Ltd, Shuozhou 036000, China;
3. Department of Automation, North China Electric Power University, Baoding 071003, China

Received Date: 2025-09-08
Available Online: 2026-11-05

Abstract

Abstract

The accurate prediction of SO₂ concentration at the outlet of the desulfurization system in thermal power plants is crucial for operation optimization and pollution control. However， due to its nonlinearity， lag and multiple interfering factors， it is difficult to accurately predict the SO₂ concentration at the outlet. A dynamic prediction model of SO₂ concentration at the exit of the desulfurization system based on a temporal convolutional network （TCN） and bidirectional long short-term memory network （BiLSTM） was proposed. Firstly， the characteristic variables were selected by analyzing the mechanism and influencing factors of desulphurization. Then， the combined feature extraction algorithm was used to screen the variables highly related to the outlet SO₂ concentration， and the redundant items were removed. Then， the optimal set of input variables was determined by using the black-winged kite algorithm （BKA） to intelligently optimize the order combination of input and output variables. Finally， based on the TCN-BiLSTM deep learning algorithm， the dynamic prediction model of SO₂ concentration at the exit of the desulfurization system was constructed. Based on the actual operation data of the power plant， the experimental results showed that： after variable screening and order optimization， the accuracy of the model was significantly improved. The proposed TCN-BiLSTM model had excellent performance， with the best average absolute percentage error of 0.94%， the average absolute error of 0.15， and the root-mean-square error of 0.24. Compared with other models， the error performance index and prediction effect had been significantly improved， showing higher prediction accuracy and stronger fitting ability， which can effectively meet the actual field needs， to achieve real-time prediction of SO₂ concentration at the exit of the desulfurization system， and to provide a reference basis for the optimization control of SO₂ emission.

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References(23)

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