《工程科学学报》录用稿，htps:/doi.org/10.13374/i,issn2095-9389.2021.05.25.005©北京科技大学2020 基于不同算法的高炉操作炉型聚类效果对比 鲁杰，闫炳基趣，赵伟，李鹏，陈栋，国宏伟 苏州大学沙钢钢铁学院，苏州，江苏，215137 ☒通信作者，E-mail:bjyan(@sudaedu.cn 摘要高炉操作炉型与高炉操作、技术经济指标等关系密切，合理的操作炉型有利手保高炉生产的优 质、低耗、高产、长寿。通过对冷却壁温度的聚类分析，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。本文分别采用K-Means、TwoStep对数据集进行聚类分析，基于两种聚类算 法的原理，结合Davies-Bouldin indicator(DBI)与Dunn indicator(DI)对聚类结果进行评价，分析不同 聚类算法间的差异，得出了在本文所选的样本数据及数据特征基础上，KMas算法聚类结果更好的结论， 该研究可为高炉炼铁大数据分析中的聚类算法选择提供有力参考 关键词高炉操作炉型：K-Means:TwoStep:聚类；Davies-Bouldin indicator:l Dunn indicator 分类号TF512 Comparison of the effect of different clustering algorithms on the clustering of management of furnace profile LU Jie,】 YAN Bing-j,ZHAO Wei LI Peng,CHEN Dong,GUO Hong-wei School of Iron and Steel,Soochow University.Suzhou,Jiangsu215137,China Corresponding author,E-mail:biya Abstract Blast furnace eration profile is closely related to blast furnace's operation,technical and economic indicators,etc.Reasonable furnace operation profile is conducive to get high-quality hot metal,low fuel consumption,high yield and longevity of blast furnace production.Through cluster analysis of the stave temperature it can effectively and reasonably characterize the change of blast furnace operation profile,which has important guiding significance for blast furnace production.K-Means,TwoStep and hierarchical clustering algorithms are most commonly used in domestic for blast furnace operation profile monitoring at this stage.The research results also show that different clustering algorithms can basically achieve the management of blast furnace operation profile,but for different algorithms,the difference among the clustering results is not clear. Based on the previous research,this paper compared the clustering principles and research status with different algorithms,and selected two algorithms of K-Means and TwoStep which were more applied and more compatible with the algorithm principles.K-Means algorithm was a typical partition-based clustering algorithm,with low time complexity,high clustering efficiency,and good clustering quality.It was widely used in cluster analysis of blast 收精日期： 基童项目：国家自然科学基金资助项目(52074185,51774209)基于不同算法的高炉操作炉型聚类效果对比 鲁 杰 ，闫炳基，赵 伟，李 鹏，陈 栋，国宏伟 苏州大学沙钢钢铁学院，苏州，江苏，215137  通信作者，E-mail: bjyan@suda.edu.cn 摘 要 高炉操作炉型与高炉操作、技术经济指标等关系密切，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。通过对冷却壁温度的聚类分析，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。本文分别采用 K-Means、TwoStep 对数据集进行聚类分析，基于两种聚类算 法的原理，结合 Davies-Bouldin indicator（DBI）与 Dunn indicator（DI）对聚类结果进行评价，分析不同 聚类算法间的差异，得出了在本文所选的样本数据及数据特征基础上，K-Means 算法聚类结果更好的结论， 该研究可为高炉炼铁大数据分析中的聚类算法选择提供有力参考。 关键词 高炉操作炉型；K-Means；TwoStep；聚类；Davies-Bouldin indicator；Dunn indicator 分类号 TF512 Comparison of the effect of different clustering algorithms on the clustering of management of furnace profile LU Jie，YAN Bing-ji，ZHAO Wei，LI Peng，CHEN Dong，GUO Hong-wei School of Iron and Steel, Soochow University, Suzhou, Jiangsu 215137, China  Corresponding author, E-mail: bjyan@suda.edu.cn Abstract Blast furnace operation profile is closely related to blast furnace's operation, technical and economic indicators, etc. Reasonable furnace operation profile is conducive to get high-quality hot metal, low fuel consumption, high yield and longevity of blast furnace production. Through cluster analysis of the stave temperature, it can effectively and reasonably characterize the change of blast furnace operation profile, which has important guiding significance for blast furnace production. K-Means, TwoStep and hierarchical clustering algorithms are most commonly used in domestic for blast furnace operation profile monitoring at this stage. The research results also show that different clustering algorithms can basically achieve the management of blast furnace operation profile, but for different algorithms, the difference among the clustering results is not clear. Based on the previous research, this paper compared the clustering principles and research status with different algorithms, and selected two algorithms of K-Means and TwoStep which were more applied and more compatible with the algorithm principles. K-Means algorithm was a typical partition-based clustering algorithm, with low time complexity, high clustering efficiency, and good clustering quality. It was widely used in cluster analysis of blast 收稿日期: 基金项目: 国家自然科学基金资助项目(52074185，51774209) 《工程科学学报》录用稿，https://doi.org/10.13374/j.issn2095-9389.2021.05.25.005 ©北京科技大学 2020 录用稿件，非最终出版稿