Online measurement of temperature field in furnace based on optical tomography

杨文虎; 牛世斌; 李翔; 荣誉佳; 王浩帆; 方顺利; 晋中华; 马帅; 舒朝晖

doi:10.19666/j.rlfd.202504058

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Online measurement of temperature field in furnace based on optical tomography

更新时间：2026-01-21

- Online measurement of temperature field in furnace based on optical tomography
- Thermal Power Generation Vol. 54, Issue 12, Pages: 102-108(2025)
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- DOI：10.19666/j.rlfd.202504058
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- Published：2025
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杨文虎, 牛世斌, 李翔, et al. Online measurement of temperature field in furnace based on optical tomography[J]. Thermal Power Generation, 2025, 54(12): 102-108.
DOI：

杨文虎, 牛世斌, 李翔, et al. 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.

摘要

作为反映燃烧过程的重要参数，炉内温度分布关系到炉内燃烧过程的安全性、经济性和污染物排放的水平，对于研究锅炉控制和炉内燃烧过程具有十分重要的意义。辐射成像法由于时空分辨率高、现场容易实施等特点，适用于炉内温度场的重建。基于此，提出了一种基于光学层析的炉内温度场在线测量技术，采用深度学习与正则化算法相结合的重建算法来解决温度场重建过程中的病态问题。首先，根据设置的炉膛尺寸、介质辐射特性、CCD摄像机安装位置等参数建立辐射成像模型，通过正问题计算获得大量数据；然后，通过自动寻优算法找到合适的Tikhonov正则化参数构建训练数据集，同时评估解的精确性和稳定性；最后，建立深度神经网络模型预测最优的正则化参数，进而对温度场进行重建。结果表明，所提出的炉内温度场重建算法的误差小于5%，准确性较好。在加入测量误差后，重建误差仍在5%之内，表明该方法具有鲁棒性。同时，该方法计算效率较高，能满足温度场实时监测的要求。

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references

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