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Advancements in the application of machine learning in circulating fluidized bed boiler technology
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Hongliang XIAO1, 2, Xiwei KE1, 2, Shuai PAN3, Liping LANG1, 2, 4, Junfeng WANG1, 2, 4, Haiying QI1, 2, 5, Shouyu ZHANG1, 2, Junfu LYU1, 2, 5, Zhong HUANG1, 2, 5
Thermal Power Generation | 2025, 54(7) : 1 - 13
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Thermal Power Generation | 2025, 54(7): 1-13
• Special topic on “ultra supercritical circulating fluidized bed power generation technology” •
Advancements in the application of machine learning in circulating fluidized bed boiler technology
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Hongliang XIAO1, 2, Xiwei KE1, 2, Shuai PAN3, Liping LANG1, 2, 4, Junfeng WANG1, 2, 4, Haiying QI1, 2, 5, Shouyu ZHANG1, 2, Junfu LYU1, 2, 5, Zhong HUANG1, 2, 5
Affiliations
  • 1.Beijing Huairou Laboratory, Beijing 101499, China
  • 2.Shanxi Research Institute of Huairou Laboratory, Taiyuan 030032, China
  • 3.College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China
  • 4.Harbin Boiler Co., Ltd., Harbin 150046, China
  • 5.Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China
Published: 2025-07-25 doi: 10.19666/j.rlfd.202411246
Outline
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Circulating fluidized bed (CFB) boilers play a pivotal role in China’s power generation landscape. However, the intricate combustion system within the CFB boiler furnace exhibits strong coupling characteristics, characterized by multiple parameters, variables, nonlinearity, and time-varying dynamics, posing a significant challenge for precise system modeling and prediction. Machine learning (ML), with its robust nonlinear processing capabilities and predictive performance, holds immense promise in the domain of CFB technology. This paper delves into the application of ML techniques in this field, encompassing the prediction of minimum fluidization velocity, emissions forecasting, bed pressure forecasting, bed temperature/thermal efficiency prediction, particle circulation rate prediction, reduced-order models of computational fluid dynamics (CFD) flow fields, and boiler safety control system models. The paper critically evaluates the strengths and limitations of these technologies in various scenarios, providing an insightful perspective on the opportunities and challenges faced by CFB boilers in the era of big data. Emphasizing aspects like model interpretability, enhancing generalization capabilities, improving data quality and diversity, integrating models with conventional methods, and experimental validation are crucial areas worth attention for future advancements.

coal-fired boiler  /  circulating fluidized bed boiler  /  machine learning  /  nonlinearity  /  prediction
Hongliang XIAO, Xiwei KE, Shuai PAN, Liping LANG, Junfeng WANG, Haiying QI, Shouyu ZHANG, Junfu LYU, Zhong HUANG. Advancements in the application of machine learning in circulating fluidized bed boiler technology[J]. Thermal Power Generation, 2025 , 54 (7) : 1 -13 . DOI: 10.19666/j.rlfd.202411246
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  • Program of Beijing Huairou Laboratory(ZD2023008A)
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Year 2025 volume 54 Issue 7
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Article Info
doi: 10.19666/j.rlfd.202411246
  • Receive Date：2024-11-13
  • Online Date：2026-03-06
  • Published：2025-07-25
Article Data
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History
  • Received：2024-11-13
Funding
Program of Beijing Huairou Laboratory(ZD2023008A)
Affiliations
    1.Beijing Huairou Laboratory, Beijing 101499, China
    2.Shanxi Research Institute of Huairou Laboratory, Taiyuan 030032, China
    3.College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China
    4.Harbin Boiler Co., Ltd., Harbin 150046, China
    5.Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China
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