基于图像识别的烧结矿落料温度拟合模型  

Sinter blanking temperature fitting model based on image recognition

作　　者：包向军 刘钟腾 张晓萍[2] 张璐 陈光 王诗歌 BAO Xiangjun;LIU Zhongteng;ZHANG Xiaoping;ZHANG Lu;CHEN Guang;WANG Shige(School of Energy and Environment,Anhui University of Technology,Ma’anshan 243002,Anhui,China;Ma’anshan Iron&Steel Company Ltd.,Ma’anshan 243000,Anhui,China)

机构地区：[1]安徽工业大学能源与环境学院,安徽马鞍山243002 [2]马鞍山钢铁股份有限公司,安徽马鞍山243000

出　　处：《烧结球团》2024年第5期110-116,共7页Sintering and Pelletizing

基　　金：安徽高校自然科学研究项目(KJ2021A0411)。

摘　　要：烧结矿落料温度是判断烧结终点、评价烧结矿质量、影响烧结余热回收效率的重要参数。针对烧结矿落料温度检测受现场环境限制,测温难度大、不稳定、精度差等问题,本文通过试验模拟烧结现场的环境条件,使用红外热成像设备提取试验图像数据作为训练样本,并使用热电偶进行温度校准作为测试样本,提出一种基于图像识别的非接触式烧结矿落料温度特征提取方法,分别建立直接拟合模型和分步拟合模型。对比分析结果表明:与测试组数据相比,基于图像特征的烧结矿温度直接拟合模型误差为3.51%,均方根误差为28.24;采用明度值拟合辐射能量再拟合烧结矿落料温度的分步拟合模型的误差为2.10%,均方根误差为17.21。采用基于图像识别的烧结矿落料温度分步拟合模型结果更稳定且精度更高,该模型对于提高烧结矿落料温度检测的准确性和稳定性具有重要的应用价值,对于优化烧结过程控制、提高余热回收效率具有潜在的推动作用。Sinter blanking temperature is an important parameter for judging the sintering endpoint,evaluating the quality of sinter,and affecting the recovery efficiency of sintering waste heat.In order to solve such problems as sinter blanking temperature detection limited by the on-site environment,difficult temperature measurement,instability and poor accuracy,the environmental conditions of the sintering site are simulated by experiments,the test image data are extracted by infrared thermal imaging equipment as the training sample,the thermocouples are used for temperature calibration as the test sample,a non-contact sinter blanking temperature feature extraction method is proposed based on image recognition,and the direct fitting model and the step-by-step fitting model respectively are established.The results show that compared with the data in the test group,the error of the direct fitting model of sinter temperature based on image features is 3.51% and the root mean square error is 28.24,and the error of the step-by-step fitting model of fitting radiant energy and then fitting sinter blanking temperature by luminosity value is 2.10%,and the root mean square error is 17.21.The step-by-step fitting model based on image recognition is more stable and accurate,which has important application value for improving the accuracy and stability of sinter blanking temperature detection,and has a potential role in optimizing the sintering process control and improving the waste heat recovery efficiency.

关 键 词：烧结矿 温度 图像识别 最小二乘法 分步拟合 

分 类 号：TF046.4[冶金工程—冶金物理化学] TP183[自动化与计算机技术—控制理论与控制工程]