基于大数据下煤气流不同发展时期温度场的定标研究  

Calibration of temperature field of gas flow in different development stages based on big data

作　　者：张生海 石琳[2] 李明昕 于涛 ZHANG Sheng-Hai;SHI Lin;LI Ming-xin;YU Tao(Materials and Metallurgy School,Inner Mongolia University of Science and Technology,Baotou 014010,China;Science School,Inner Mongolia University of Science and Technology,Baotou 014010,China)

机构地区：[1]内蒙古科技大学材料与冶金学院,内蒙古包头014010 [2]内蒙古科技大学理学院,内蒙古包头0140101

出　　处：《内蒙古科技大学学报》2018年第4期343-348,共6页Journal of Inner Mongolia University of Science and Technology

基　　金：国家自然科学基金资助项目(61263015);内蒙古自然科学基金资助项目(2017MS(LH)0104;2018LH01009)

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.

关 键 词：大数据 最小二乘法 图像特征 温度场定标 

分 类 号：TF57[冶金工程—钢铁冶金]