周科,王健,任延南,张越,戴轩,梁传龙,金全,闫伟杰

• 1:西安热工研究院有限公司
• 2:华能国际电力股份有限公司丹东电厂
• 3:南京理工大学能源与动力工程学院

摘要(Abstract)：

现有火焰辐射图像测温技术因探测器镜头结焦问题导致测量误差，亟需一种能够智能消除结焦干扰的在线监测方法。提出一种融合火焰辐射图像和卷积神经网络（convolutional neural network,CNN）的电站锅炉温度场在线监测方法。首先，通过黑体炉标定探测器，建立探测器单色辐射强度与图像强度的关系；然后，设计适合火焰图像处理的CNN模型，并利用现场采集锅炉的未结焦火焰辐射强度图像构建学习集，建立火焰辐射强度图像还原模型；最后，利用模拟结焦火焰图像验证该方法的测量精度。研究结果表明：测温精度随着学习集数量的减少而降低，学习集火焰图像数量为3 000张时，测温相对误差为1.4%；测温精度随着结焦面积的增大而降低，结焦面积为30%时，测量温度的最大相对误差为0.7%。此外，研究表明学习集训练探测器的模型计算其他探测器结焦图像时，测温误差会增大，最大相对误差达34.6%。这表明应用该方法时需对每个燃烧器的探测器单独训练。研究方法能够智能消除结焦对火焰辐射图像的干扰，实现高精度温度场在线监测，为电站锅炉的安全运行和燃烧优化提供了可靠的技术支持。

关键词(KeyWords)： 辐射测温;温度场;煤粉火焰图像;电站锅炉;卷积神经网络

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金面上项目（52176144）~~

作者(Author): 周科,王健,任延南,张越,戴轩,梁传龙,金全,闫伟杰

DOI: 10.19666/j.rlfd.202504094

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