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基于光学层析的炉内温度场在线测量研究

YANG Wenhu NIU Shibin LI Xiang RONG Yujia WANG Haofan FANG Shunli JIN Zhonghua MA Shuai SHU Zhaohui

热力发电2025,Vol.54Issue(12):102-108,7.
热力发电2025,Vol.54Issue(12):102-108,7.DOI:10.19666/j.rlfd.202504058

基于光学层析的炉内温度场在线测量研究

Online measurement of temperature field in furnace based on optical tomography

YANG Wenhu 1NIU Shibin 1LI Xiang 1RONG Yujia 1WANG Haofan 2FANG Shunli 2JIN Zhonghua 2MA Shuai 3SHU Zhaohui3

作者信息

  • 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
  • 折叠

摘要

Abstract

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.

关键词

燃煤锅炉/温度场/光学层析/在线测量/深度神经网络

Key words

coal-fired boiler/temperature field/optical tomography/online measurement/deep neural network

引用本文复制引用

YANG Wenhu,NIU Shibin,LI Xiang,RONG Yujia,WANG Haofan,FANG Shunli,JIN Zhonghua,MA Shuai,SHU Zhaohui..基于光学层析的炉内温度场在线测量研究[J].热力发电,2025,54(12):102-108,7.

基金项目

国家重点研发计划项目(2024YFB4104804)National Key Research and Development Program(2024YFB4104804) (2024YFB4104804)

热力发电

OA北大核心

1002-3364

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