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Review of furnace temperature field online monitoring and prediction for deep peaking and smart power generation
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Shunli FANG1, Zhonghua JIN1, Yun YANG2, Xiang LI3, Shipeng REN4, Shuai MA4, Bin YAO4, Haofan WANG1, Zhonghui ZHANG1, Shengdong MEI5, Kai LIU5, Xinjian CHEN5, Chun LOU4, Ying ZOU2
Thermal Power Generation | 2025, 54(4) : 13 - 23
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Thermal Power Generation | 2025, 54(4): 13-23
Technical and economic review
Review of furnace temperature field online monitoring and prediction for deep peaking and smart power generation
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Shunli FANG1, Zhonghua JIN1, Yun YANG2, Xiang LI3, Shipeng REN4, Shuai MA4, Bin YAO4, Haofan WANG1, Zhonghui ZHANG1, Shengdong MEI5, Kai LIU5, Xinjian CHEN5, Chun LOU4, Ying ZOU2
Affiliations
  • 1.Xi’an Thermal Power Research Institute Co., Ltd., Xi’an 710054, China
  • 2.Xi’an Jiaotong University, Xi’an 710049, China
  • 3.Lanzhou Aluminum Industry Co., Ltd., Lanzhou 730070, China
  • 4.State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan 430074, China
  • 5.Wuhan Leaway Engineering & Technology Co., Ltd., Wuhan 430223, China
Published: 2025-04-25 doi: 10.19666/j.rlfd.202408174
Outline
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When thermal power units participate in deep peak loading, real-time acquisition of furnace temperature field is helpful to power plant boiler control and research of combustion process in the furnace. With the promotion of intelligent power generation, machine learning provides an important means for real-time acquisition of furnace temperature field. The principle and application of the three most commonly used online monitoring technologies of furnace temperature field, namely acoustic method, absorption spectral tomography and thermal radiation imaging, are summarized at first, and the advantages and disadvantages in the application of boiler furnace temperature measurement are reviewed. Then, the principle of the coupled machine learning and CFD prediction method is described in detail, indicating that the method is less affected in the harsh furnace environment, and the application research of the method in the combustion flame structure and parameters and the furnace temperature field is reviewed, demonstrating the feasibility of applying the method to the furnace temperature field, indicating it can accurately predict the furnace temperature field. Finally, the future development trend of furnace temperature field online monitoring technology and coupled machine learning and CFD prediction method is analyzed, so as to provide ideas for obtaining more accurate furnace temperature field in real time under the continuous advancement of intelligent construction of power station.

utility boiler  /  furnace temperature field  /  online monitoring  /  machine learning  /  prediction
Shunli FANG, Zhonghua JIN, Yun YANG, Xiang LI, Shipeng REN, Shuai MA, Bin YAO, Haofan WANG, Zhonghui ZHANG, Shengdong MEI, Kai LIU, Xinjian CHEN, Chun LOU, Ying ZOU. Review of furnace temperature field online monitoring and prediction for deep peaking and smart power generation[J]. Thermal Power Generation, 2025 , 54 (4) : 13 -23 . DOI: 10.19666/j.rlfd.202408174
  • National Key Research and Development Program(2022YFB4100703)
Year 2025 volume 54 Issue 4
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Article Info
doi: 10.19666/j.rlfd.202408174
  • Receive Date:2024-08-19
  • Online Date:2026-03-06
  • Published:2025-04-25
Article Data
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History
  • Received:2024-08-19
Funding
National Key Research and Development Program(2022YFB4100703)
Affiliations
    1.Xi’an Thermal Power Research Institute Co., Ltd., Xi’an 710054, China
    2.Xi’an Jiaotong University, Xi’an 710049, China
    3.Lanzhou Aluminum Industry Co., Ltd., Lanzhou 730070, China
    4.State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan 430074, China
    5.Wuhan Leaway Engineering & Technology Co., Ltd., Wuhan 430223, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
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占总种数比例
Percentage of
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种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
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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