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Online measurement of temperature field in furnace based on optical tomography
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Wenhu YANG1, Shibin NIU1, Xiang LI1, Yujia RONG1, Haofan WANG2, Shunli FANG2, Zhonghua JIN2, Shuai MA3, Zhaohui SHU3
Thermal Power Generation | 2025, 54(12) : 102 - 108
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Thermal Power Generation | 2025, 54(12): 102-108
Combustion optimization and intelligent operation
Online measurement of temperature field in furnace based on optical tomography
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Wenhu YANG1, Shibin NIU1, Xiang LI1, Yujia RONG1, Haofan WANG2, Shunli FANG2, Zhonghua JIN2, Shuai MA3, Zhaohui SHU3
Affiliations
  • 1.Lanzhou Aluminium Industry Co., Ltd., Lanzhou 730070, China
  • 2.Xi’an Thermal Power Research Institute Co., Ltd., Xi’an 710054, China
  • 3.School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Published: 2025-12-25 doi: 10.19666/j.rlfd.202504058
Outline
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As an important parameter reflecting the combustion process, temperature distribution in a furnace is related to the safety, economy and pollutant emission level of the combustion process, which is of great significance for boiler control and the study of the combustion process in the furnace. The radiation imaging method is suitable for reconstruction of furnace temperature field due to its high temporal and spatial resolution and easy implementation on site. An online measurement technology of furnace temperature field based on optical tomography is proposed. A reconstruction algorithm combining deep learning with regularization algorithm is adopted to solve the ill-posed problem in the temperature field reconstruction process. Firstly, a radiation imaging model is established according to the set parameters such as furnace size, medium radiation characteristics, and CCD camera installation position. A large amount of data is obtained through direct problem calculation. Then, the appropriate Tikhonov regularization parameter is found through an automatic optimization algorithm to construct the training data set, and the accuracy and stability of the solution are evaluated. Finally, a deep neural network model is established to predict the optimal regularization parameter and then reconstruct the temperature field. The results show that this furnace temperature field reconstruction algorithm has an error less than 5%, showing good accuracy. After adding the measurement error, the reconstruction error is within 5%, indicating that the method is robust. At the same time, this method has high computational efficiency and meets the requirements of real-time monitoring of temperature fields.

coal-fired boiler  /  temperature field  /  optical tomography  /  online measurement  /  deep neural network
Wenhu YANG, Shibin NIU, Xiang LI, Yujia RONG, Haofan WANG, Shunli FANG, Zhonghua JIN, Shuai MA, Zhaohui SHU. Online measurement of temperature field in furnace based on optical tomography[J]. Thermal Power Generation, 2025 , 54 (12) : 102 -108 . DOI: 10.19666/j.rlfd.202504058
  • National Key Research and Development Program(2024YFB4104804)
Year 2025 volume 54 Issue 12
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Article Info
doi: 10.19666/j.rlfd.202504058
  • Receive Date:2025-04-20
  • Online Date:2026-01-13
  • Published:2025-12-25
Article Data
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History
  • Received:2025-04-20
Funding
National Key Research and Development Program(2024YFB4104804)
Affiliations
    1.Lanzhou Aluminium Industry Co., Ltd., Lanzhou 730070, China
    2.Xi’an Thermal Power Research Institute Co., Ltd., Xi’an 710054, China
    3.School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, 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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占总种数比例
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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