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Modeling of SCR denitrification reactor for thermal power units based on　improved grey wolf optimization LSTM network
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Lei WU1, Hua GU1, Yiming YAO1, Jun ZHANG2, 3, Jun SU1, Yi CHEN1
Thermal Power Generation | 2025, 54(11) : 136 - 141
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Thermal Power Generation | 2025, 54(11): 136-141
• Thermal energy science research •
Modeling of SCR denitrification reactor for thermal power units based on　improved grey wolf optimization LSTM network
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Lei WU1, Hua GU1, Yiming YAO1, Jun ZHANG2, 3, Jun SU1, Yi CHEN1
Affiliations
  • 1.State Grid Shanghai Qingpu Electric Power Supply Company, Shanghai 201700, China
  • 2.School of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
  • 3.Shanghai Key Laboratory of Power Station Automation Technology, Shanghai 200090, China
Published: 2025-11-25 doi: 10.19666/j.rlfd.202502032
Outline
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A hybrid prediction model combining enhanced grey wolf optimization algorithm (EGWO) and long short-term memory (LSTM) neural network is proposed to address the problem of low accuracy in predicting the mass concentration of NOx at the outlet of selective catalytic reduction (SCR) denitrification reactors using conventional mechanism modeling methods. Firstly, based on principal component analysis (PCA), the raw data is processed and filtered to achieve dimensionality reduction of input variables. Then, the EGWO is used to optimize the hyperparameters of LSTM. Finally, the input variables are used as inputs for the EGWO-LSTM model to predict the mass concentration of NOx at the outlet. Taking a 1 000 MW ultra supercritical thermal power unit in China as an example, simulation results show that the proposed model performs the best in error control, with root mean square error reduces by 50.36% compared to the conventional LSTM model, and by 76.14% compared to the BP model, and the mean absolute percentage error of the model is only 1.01%. The EGWO has fewer iterations and higher convergence accuracy compared to the GWO when converging to the optimal solution.

SCR denitration reactor  /  prediction model  /  NOx  /  LSTM  /  principal component analysis  /  enhanced grey wolf optimization algorithm
Lei WU, Hua GU, Yiming YAO, Jun ZHANG, Jun SU, Yi CHEN. Modeling of SCR denitrification reactor for thermal power units based on　improved grey wolf optimization LSTM network[J]. Thermal Power Generation, 2025 , 54 (11) : 136 -141 . DOI: 10.19666/j.rlfd.202502032
Funding
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  • National Natural Science Foundation of China(61273190)
  • Funding Project of Shanghai Key Laboratory of Power Station Automation Technology(13DZ2273800)
Appendix
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Year 2025 volume 54 Issue 11
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Article Info
doi: 10.19666/j.rlfd.202502032
  • Receive Date：2025-02-24
  • Online Date：2026-01-13
  • Published：2025-11-25
Article Data
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History
  • Received：2025-02-24
Funding
National Natural Science Foundation of China(61273190)
Funding Project of Shanghai Key Laboratory of Power Station Automation Technology(13DZ2273800)
Affiliations
    1.State Grid Shanghai Qingpu Electric Power Supply Company, Shanghai 201700, China
    2.School of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
    3.Shanghai Key Laboratory of Power Station Automation Technology, Shanghai 200090, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202502032
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