基于大数据下煤气流不同发展时期温度场的定标研究
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摘要
针对高炉温度场较难定标的问题,提出了一种基于大数据下煤气流在不同发展阶段灰度值与温度的定标关系.首先,通过高炉红外图像特征分析,确定高炉生产过程中煤气流发展的不同状态;然后,根据红外图像各个像素点与拍摄区域对应关系,确定十字测温热电偶在图像中的像素坐标;最后,根据某高炉一年的生产数据,通过最小二乘法拟合得到煤气流在3个发展状态下的温度与灰度的对应关系,为高炉温度场分布研究提供指导.
Aiming at the problem that the temperature field of blast furnace is difficult to scale,a calibration relationship between gray value and temperature of gas flow in different development stages was proposed based on big data. Firstly,the different states of gas flow development in the blast furnace production process were determined through the analysis of blast furnace infrared image feature;then,the pixel coordinates of the cross-temperature thermocouple in the image were determined according to the corresponding relationship between the pixel points of the infrared image and the shooting area. According to the one-year production data of a blast furnace,the corresponding relationship between temperature and gradation of gas flow in three development states was obtained by using the least squares fitting method,providing guidance for the study of the temperature field distribution of blast furnace.
Aiming at the problem that the temperature field of blast furnace is difficult to scale,a calibration relationship between gray value and temperature of gas flow in different development stages was proposed based on big data. Firstly,the different states of gas flow development in the blast furnace production process were determined through the analysis of blast furnace infrared image feature;then,the pixel coordinates of the cross-temperature thermocouple in the image were determined according to the corresponding relationship between the pixel points of the infrared image and the shooting area. According to the one-year production data of a blast furnace,the corresponding relationship between temperature and gradation of gas flow in three development states was obtained by using the least squares fitting method,providing guidance for the study of the temperature field distribution of blast furnace.
引文
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[3]许永华,吴敏,曹卫华,等.基于图像灰度统计分布的高炉温度场动态定标算法[J].冶金自动化,2007,31(3):40-43,61.
[4]宁志宇.高炉温度场与料面分布模型及其应用研究[D].长沙:中南大学,2005.
[5]胡晓元,史浩山. Win Pcap包截获系统的分析及其应用[J].计算机工程,2005:31(2):96-98.
[6]安剑奇.基于多源信息融合的高炉料面温度场在线检测系统研究及其应用[D].长沙:中南大学,2011.
[7]安剑奇,吴敏,何勇,等.基于分层递阶融合算法的高炉料面煤气流分布软测量方法[J].自动化学报,2011,37(4):496-502.
[8]吴敏,王昌军,安剑奇,等.基于料面温度场的高炉煤气流分布识别方法[J].信息与控制,2011,40(1):78-82.
[9]魏承文.汽车牌照识别技术的研究[D].广州:华南理工大学,2014.
[10]刘文磊.高炉CO利用率与煤气流分布关系模型研究[D].包头:内蒙古科技大学,2016.
[11]李晓飞,马大玮,粘永健,等.图像腐蚀和膨胀的算法研究[J].影像技术,2005,(1):37-39.