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基于红外与可见光视觉的高炉铁口铁水温度场在线检测

潘冬 许川 龚芃旭 蒋朝辉 桂卫华

潘冬, 许川, 龚芃旭, 蒋朝辉, 桂卫华. 基于红外与可见光视觉的高炉铁口铁水温度场在线检测. 自动化学报, 2025, 51(2): 1−13 doi: 10.16383/j.aas.c240378
引用本文: 潘冬, 许川, 龚芃旭, 蒋朝辉, 桂卫华. 基于红外与可见光视觉的高炉铁口铁水温度场在线检测. 自动化学报, 2025, 51(2): 1−13 doi: 10.16383/j.aas.c240378
Pan Dong, Xu Chuan, Gong Peng-Xu, Jiang Zhao-Hui, Gui Wei-Hua. Online measurement of molten iron temperature field at blast furnace taphole based on infrared and visible vision. Acta Automatica Sinica, 2025, 51(2): 1−13 doi: 10.16383/j.aas.c240378
Citation: Pan Dong, Xu Chuan, Gong Peng-Xu, Jiang Zhao-Hui, Gui Wei-Hua. Online measurement of molten iron temperature field at blast furnace taphole based on infrared and visible vision. Acta Automatica Sinica, 2025, 51(2): 1−13 doi: 10.16383/j.aas.c240378

基于红外与可见光视觉的高炉铁口铁水温度场在线检测

doi: 10.16383/j.aas.c240378 cstr: 32138.14.j.aas.c240378
基金项目: 国家自然科学基金(62303491), 湘江实验室重大项目(22XJ01005), 湖南省科技创新计划(2024RC1007), 工业控制技术全国重点实验室开放课题(ICT2024B05)资助
详细信息
    作者简介:

    潘冬:中南大学自动化学院副教授. 主要研究方向为红外热成像, 视觉检测, 深度学习, 图像处理, 误差建模与补偿. E-mail: pandong@csu.edu.cn

    许川:中南大学自动化学院博士研究生. 主要研究方向为图像处理, 数据分析, 深度学习和复杂工业过程建模. 本文通信作者. E-mail: csuxuchuan@csu.edu.cn

    龚芃旭:中南大学自动化学院硕士研究生. 主要研究方向为图像处理,数据分析, 机器学习和复杂工业过程建模. E-mail: agongpxz@163.com

    蒋朝辉:中南大学自动化学院教授. 主要研究方向为检测技术与自动化装置, 图像处理, 工业VR以及复杂工业过程的建模和优化控制. E-mail: jzh0903@csu.edu.cn

    桂卫华:中南大学自动化学院教授. 主要研究方向为复杂工业过程检测、建模与控制. E-mail: gwh@csu.edu.cn

Online Measurement of Molten Iron Temperature Field at Blast Furnace Taphole Based on Infrared and Visible Vision

Funds: Supported by National Natural Science Foundation of China (62303491), Major Program of Xiangjiang Laboratory (22XJ01005), Science and Technology Innovation Program of Hunan Province (2024RC1007), and National Key Laboratory of Industrial Control Technology Open Project (ICT2024B05)
More Information
    Author Bio:

    PAN Dong Associate professor at the School of Automation, Central South University. His research interest covers infrared thermography, vision-based measurement, image processing, error modeling and compensation

    XU Chuan Ph.D. candidate at the School of Automation, Central South University. His research interest covers image processing, data analysis, deep learning and complex industrial process modeling. Corresponding author ofthis paper

    GONG Peng-Xu Master student at the School of Automation, Central South University. His research interest covers image processing, data analysis, machine learning and complex industrial process modeling

    JIANG Zhao-Hui Professor at the School of Automation, Central South University. His research interest covers detection technology and automatic equipment, image processing, industrial VR, modeling and optimal control of complex industrial processes

    GUI Wei-Hua Professor at the School of Automation, Central South University. His research interest covers measurement, modeling and control of complex industrial process

  • 摘要: 高炉铁口铁水温度场 (Molten iron temperature field, MITF) 是表征铁水质量、判断炉温状况的重要信息. 然而高炉出铁场动态粉尘的干扰使得铁水温度场的在线准确获取充满挑战. 为此, 首次提出基于红外与可见光视觉的高炉铁口铁水温度场检测方法, 利用可见光图像为红外视觉测温提供先验粉尘干扰情况. 首先, 设计红外与可见光视觉协同的测温系统, 同步获取高炉出铁口铁水流的红外图像和可见光图像, 铁水流红外图像表征铁水原始温度场信息, 可见光图像为量化粉尘透射率提供数据基础. 其次, 构建基于色彩一致性的可见光图像中粉尘透射率估计模型和基于雾线先验的红外图像中粉尘透射率估计模型, 得到红外波段下粉尘透射率. 最后, 结合红外辐射测温原理, 构建基于粉尘透射率的红外测温近似补偿模型, 实现铁水温度场的针对性补偿, 获取误差较小的铁水温度. 工业实验表明, 相比于仅利用红外视觉测量铁水温度场, 所提方法能够显著降低粉尘造成的测温误差, 为高炉调控提供连续可靠的铁水温度数据.
  • 图  1  高炉出铁过程示意图

    Fig.  1  Schematic diagram of blast furnace tapping process

    图  2  红外−可见视觉协同检测系统

    Fig.  2  Infrared-visible vision cooperative measurement system

    图  3  出铁口处铁水流图

    Fig.  3  Molten iron flow images at the taphole

    图  4  铁水流温度场检测方法的整体框架

    Fig.  4  Overall framework of molten iron flow temperature field measurement method

    图  5  受粉尘干扰的铁水流图像

    Fig.  5  Molten iron flow image under dust interference

    图  6  基于雾线先验的粉尘透射率场估计

    Fig.  6  Estimation of dust transmittance field based on haze-line prior

    图  7  色彩通道变换

    Fig.  7  Color channel transformation

    图  8  雾线先验确定$ A $和$ J\left( x,\;y \right) $

    Fig.  8  Determination of $ A $ and $ J\left( x,\;y \right) $ using haze-line prior

    图  9  光源渲染

    Fig.  9  Light source rendering

    图  10  透射率场估计

    Fig.  10  Transmittance field Estimation

    图  11  高炉铁口铁水流图像的透射率场估计结果和去除粉尘干扰后的图像

    Fig.  11  Transmittance field results of molten iron flow image at blast furnace taphole and the image after removing dust interference

    图  12  铁水流温度场

    Fig.  12  Temperature field of molten iron flow

    图  13  不同出铁周期得到的铁水温度

    Fig.  13  Molten iron temperature obtained at different tapping periods

    图  14  不同出铁周期测温绝对误差

    Fig.  14  Absolute error of temperature measurement at different tapping periods

    图  15  高炉铁水流温度场监控软件界面

    Fig.  15  Monitoring software interface of MITF of blast furnace

    表  1  不同方法去除粉尘对铁水流干扰后的图像性能指标

    Table  1  Performance indexes of molten iron flow images after removing dust interference using different methods

    方法 FADE ENVR NIQE VIF STD
    DCID 0.6121 1.0568 5.7528 0.8151 42.6041
    NHRG 0.4867 5.9504 5.7233 1.1632 59.4452
    NLID 0.4343 2.6741 5.5326 1.1110 50.6340
    SLID 0.4839 3.3695 5.0503 1.0318 52.0414
    本文方法 0.3348 2.4984 5.9588 1.0712 25.9634
    下载: 导出CSV

    表  2  不同测温方法的性能指标对比

    Table  2  Comparison of performance indexes of different temperature measurement methods

    测温方法 $ {M E}_{\text{max }} $(℃) $ {M E}_{\text{min }} $(℃) $ {M E}_{\text{avg}} $(℃) $ {M E}_{\text{std}} $(℃)
    红外测温 37.4963 4.9702 14.5571 8.4503
    本文方法 12.5136 1.4967 6.5563 3.7164
    下载: 导出CSV
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