热力发电

融合火焰辐射图像和CNN的电站锅炉温度场在线监测研究

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周科 ¹,²,³,⁴,⁵,⁶ , 王健 ¹,²,³,⁴,⁵,⁶ , 任延南 ¹,²,³,⁴,⁵,⁶ , 张越 ¹,²,³,⁴,⁵,⁶ , 戴轩 ¹,²,³,⁴,⁵,⁶ , 梁传龙 ¹,²,³,⁴,⁵,⁶ , 金全 ¹,²,³,⁴,⁵,⁶ , 闫伟杰 ¹,²,³,⁴,⁵,⁶

热力发电 | 2026,55(1): 142-151

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热力发电 | 2026, 55(1): 142-151

融合火焰辐射图像和CNN的电站锅炉温度场在线监测研究

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周科¹,²,³,⁴,⁵,⁶, 王健¹,²,³,⁴,⁵,⁶, 任延南¹,²,³,⁴,⁵,⁶, 张越¹,²,³,⁴,⁵,⁶, 戴轩¹,²,³,⁴,⁵,⁶, 梁传龙¹,²,³,⁴,⁵,⁶, 金全¹,²,³,⁴,⁵,⁶, 闫伟杰¹,²,³,⁴,⁵,⁶

作者信息

^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

^4.辽宁丹东 118300

^5.江苏南京 210094

^6.陕西西安 710054

Research on online monitoring of flame temperature field in power plant boilers by integrating radiation imaging and convolutional neural networks

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出版时间: 2026-01-25 doi: 10.19666/j.rlfd.202504094

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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

辐射测温 / 温度场 / 煤粉火焰图像 / 电站锅炉 / 卷积神经网络

Abstract

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The existing flame radiation image temperature measurement technology has measurement errors due to the coking problem of the detector lens. There is an urgent need for an online monitoring method that can intelligently eliminate the coking interference. An online monitoring method for the temperature field of power station boilers that integrates flame radiation images and convolutional neural network (CNN) is proposed. Firstly, the detector is calibrated via a blackbody furnace, and the relationship between the monochromatic radiation intensity of the detector and the image intensity is established. Secondly, a CNN model suitable for flame image processing is designed, and the training set is constructed by using the non-coking flame radiation intensity images of the boiler collected on-site to establish the flame radiation intensity image restoration model. Finally, the measurement accuracy of this method is verified by using the simulated coking flame images. The results show that the temperature measurement accuracy decreases with the reduction of the number of training sets. When the number of flame images in the learning set is 3 000, the relative error of temperature measurement is 1.4%. The temperature measurement accuracy decreases as the coking area increases. When the coking area is 30%, the maximum relative error of temperature measurement is 0.7%. Furthermore, studies show that when the model of the detector trained by the learning set calculates the coked images of other detectors, the temperature measurement error will increase, with the maximum relative error reaching 34.6%. This indicates that when applying this method, the detectors of each burner need to be trained separately. The proposed method can intelligently eliminate the interference of coking on the flame radiation image, achieve high-precision online monitoring of the temperature field, and provide reliable technical support for the safe operation and combustion optimization of power station boilers.

Key words

radiation thermometry / temperature field / pulverized coal flame images / power plant boiler / convolutional neural network

引用本文

周科, 王健, 任延南, 张越, 戴轩, 梁传龙, 金全, 闫伟杰. 融合火焰辐射图像和CNN的电站锅炉温度场在线监测研究. 热力发电, 2026 , 55 (1) : 142 -151 . DOI: 10.19666/j.rlfd.202504094

. Research on online monitoring of flame temperature field in power plant boilers by integrating radiation imaging and convolutional neural networks[J]. Thermal Power Generation, 2026 , 55 (1) : 142 -151 . DOI: 10.19666/j.rlfd.202504094

基金

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国家自然科学基金面上项目

参考文献

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2026年第55卷第1期

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文章信息

doi: 10.19666/j.rlfd.202504094

接收时间：2025-04-20
首发时间：2025-11-13
出版时间：2026-01-25

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出版历史

收稿日期：2025-04-20
修回日期：2025-06-30
录用日期：2025-07-02

基金

国家自然科学基金面上项目

作者信息

^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

^4.辽宁丹东 118300

^5.江苏南京 210094

^6.陕西西安 710054

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https://castjournals.cast.org.cn/joweb/rlfd/CN/10.19666/j.rlfd.202504094

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
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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