基于高炉冷却壁温度的高炉炉型聚类分析  

Clustering analysis of blast furnace profile based on blast furnace cooling stave temperature

作　　者：杜屏 雷鸣 江德文 王振阳[3] 张建良[3] 徐震 DU Ping;LEI Ming;JIANG Dewen;WANG Zhenyang;ZHANG Jianliang;XU Zhen(Ironmaking Office,Shagang Group Co.,Ltd.,Zhangjiagang 215625,China;Institute of Research of Iron&Steel,Shagang,Shagang,Jiangsu Province,Zhangjiagang 215625,China;School of Metallurgical and Ecological Engineering,University of Science and Technology Beijing,Beijing 100083,China;Jiangsu Jicui Metallurgical Technology Research Institute Co.,Ltd.,Zhangjiagang 215625,China)

机构地区：[1]沙钢集团有限公司铁前办,江苏张家港215625 [2]江苏省(沙钢)钢铁研究院,江苏张家港215625 [3]北京科技大学冶金与生态工程学院,北京100083 [4]江苏集萃冶金技术研究院有限公司,江苏张家港215625

出　　处：《冶金自动化》2023年第3期109-115,共7页Metallurgical Industry Automation

摘　　要：高炉操作炉型与高炉长寿、高炉操作及技术经济指标等密切相关,合理的操作炉型有利于保证高炉生产的优质、低耗、高产、长寿。通过对高炉冷却壁温度数据的聚类分析,能够有效合理地表征高炉操作炉型的变化,对高炉生产有着重要的指导意义。基于沙钢5800 m3高炉冷却壁温度数据,分别采用K均值聚类(K-means)、高斯混合模型(Gaussian mixture model,GMM)对数据集进行聚类分析,基于两种聚类算法,结合戴维斯-唐纳德指数(Davies-Bouldin indicator,DBI)与轮廓系数(Silhouette coefficient,SC)对聚类结果进行评价,并分析了所得聚类簇类别对应生产状态的高炉冶炼情况。得出了在本文所选的样本数据基础上,采用K-means算法且当炉型聚类为3时聚类结果更好,且第3类炉型对应的平均焦比、煤比、燃料比、煤气利用率、铁水温度及产量分别为357.62 kg/t、163.18 kg/t、512.34 kg/t、47.51%、1502.045℃、12472.59 t/d,更适合该高炉日常生产的结论。该研究可为高炉炼铁冶炼过程的大数据分析聚类算法的选择及聚类结果分析评价提供一定参考。The blast furnace operation profile is closely related to blast furnace long life,blast furnace operation and technical and economic indexes,etc.A reasonable operating furnace type is helpful to ensure high quality,low consumption,high production and long life of blast furnace production.The clustering analysis of the cooling stave temperature data of blast furnace can effectively and reasonably characterize the changes of blast furnace operation profile,which has important guiding significance for the blast furnace production.K-means and Gaussian mixture model(GMM)were used to cluster the data set based on cooling stave temperature data of Shasteel 5800 m3 blast furnace,and the clustering results were evaluated based on the principles of the two clustering algorithms,combined with Davies-Bouldin indicator(DBI)and Silhouette coefficient(SC),and analyzed the blast furnace smelting situation corresponding to the production status of the obtained cluster categories.The clustering results are better when the K-mean clustering algorithm is used and the furnace profile is clustered as 3,based on the sample data selected in this paper.And the average coke ratio,coal ratio,fuel ratio,gas utilization rate,hot metal temperature and output of the 3rd furnace profile corresponding to 357.62 kg/t,163.18 kg/t,512.34 kg/t,47.51%,1502.045℃and 12472.59 t/d,respectively,so it is more suitable for the daily production of this blast furnace.This study can provide a strong reference for the selection of clustering algorithm and the evaluation of clustering results in the analysis of blast furnace ironmaking big data.

关 键 词：高炉炉型 冷却壁温度 聚类 炼铁大数据 

分 类 号：TF57[冶金工程—钢铁冶金]