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Prediction of key indicators of utility boiler based on multi-task uncertainty loss
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Yufei WANG1, Nan LI1, Gang XIE1, Xiaohong ZHANG1, Xiaoyin NIE1, Yong ZHOU2
Thermal Power Generation | 2025, 54(5) : 132 - 139
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Thermal Power Generation | 2025, 54(5): 132-139
• Power generation technology •
Prediction of key indicators of utility boiler based on multi-task uncertainty loss
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Yufei WANG1, Nan LI1, Gang XIE1, Xiaohong ZHANG1, Xiaoyin NIE1, Yong ZHOU2
Affiliations
  • 1.Shanxi Key Laboratory of Advanced Control and Industrial Intelligence, Taiyuan University of Science and Technology, Taiyuan 030024, China
  • 2.Shanxi Huaguang Power Generation Co., Ltd., Lüliang 033399, China
Published: 2025-05-25 doi: 10.19666/j.rlfd.202408222
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With the increasing demand for flexible operation of power plant boilers, frequent variable-load operation leads to a wide range of fluctuations in pollutant concentrations and flue gas parameters. Modeling of key indicators such as single pollutant or flue gas parameter can no longer meet the actual demand, so it is necessary to consider the coupling of multiple key indicators for synergistic predictive modeling. Based on the historical operation data of coal-fired power plants, feature extraction is performed through kernel function mapping, and a long short-term memory neural network with a hard parameter sharing structure is constructed for multi task prediction modeling. The prediction model is optimized using uncertainty loss methods. The experimental results show that, the proposed prediction model exhibits high prediction accuracy under variable load conditions, and the prediction errors for the key metrics involved in this study are reduced by 25.5%, 41.8% and 4.7%, respectively. The proposed method is capable of predicting several key indicators of utility boilers under variable load conditions, which can assist power plants to achieve pollution control and optimize the thermal efficiency of combustion, and provide technical support for intelligent operation of power plants.

coal-fired boiler  /  prediction model  /  LSTM neural network  /  multi-task learning  /  uncertainty loss
Yufei WANG, Nan LI, Gang XIE, Xiaohong ZHANG, Xiaoyin NIE, Yong ZHOU. Prediction of key indicators of utility boiler based on multi-task uncertainty loss[J]. Thermal Power Generation, 2025 , 54 (5) : 132 -139 . DOI: 10.19666/j.rlfd.202408222
Funding
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  • Major Science and Technology Project of Shanxi Province(202201090301013)
  • Innovation Project Foundation for Postgraduates in Shanxi Province(2023KY637)
Appendix
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Year 2025 volume 54 Issue 5
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Article Info
doi: 10.19666/j.rlfd.202408222
  • Receive Date：2024-08-28
  • Online Date：2026-03-06
  • Published：2025-05-25
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History
  • Received：2024-08-28
Funding
Major Science and Technology Project of Shanxi Province(202201090301013)
Innovation Project Foundation for Postgraduates in Shanxi Province(2023KY637)
Affiliations
    1.Shanxi Key Laboratory of Advanced Control and Industrial Intelligence, Taiyuan University of Science and Technology, Taiyuan 030024, China
    2.Shanxi Huaguang Power Generation Co., Ltd., Lüliang 033399, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202408222
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光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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