基于不同算法的高炉操作炉型聚类效果对比

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.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 index(DBI)与Dunn index(DI)对聚类结果进行评价，分析不同聚类算法间的差异，得出了在所选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.

Comparison of the effect of various clustering algorithms on the furnace profile management

The blast furnace operation profile is closely related to the operation, technical and economic indicators of a blast furnace. A reasonable furnace operation profile ensures high-quality hot metal, low fuel consumption, high yield, and furnace longevity. To guide the blast furnace ironmaking, cluster analysis of the stave temperature is implemented to effectively characterize the changes in the furnace operation profile. The K-Means, TwoStep, and hierarchical clustering algorithms are often used to monitor the blast furnace operation profile. The present study also shows that various clustering algorithms can help manage the blast furnace operation profile. However, the difference among the clustering results from these algorithms remains unclear. Based on the previous research, this paper compared the clustering principles and research status with various algorithms and selected two algorithms of K-Means and TwoStep, which were more applicable and compatible with the algorithm principles. The K-Means algorithm is a typical partition-based clustering algorithm with low time complexity, high clustering efficiency, and good clustering quality. It has been widely used in cluster analysis of the blast furnace operation profile. Additionally, domestic scholars had given effective improvement measures for the shortcomings of sensitivity to the initial center and requirements for data distribution. The TwoStep algorithm was an improved BIRCH (Balanced iterative reducing and clustering using hierarchies) algorithm, which reduced time complexity and can automatically determine the optimal number of clusters. The authors of this article considered the problem that indicators for evaluating the furnace operation profile were multiple and largely overlapped. Principal Component Analysis was introduced based on the TwoStep algorithm. Three new core indicators were generated from the traditional evaluation indicators for the clustering results of the furnace operation profile. For blast furnace operation profile monitoring and management, three core indicators also showed improved performance. In this paper, K-Means and TwoStep were used to cluster the data set. Based on the principles of these algorithms and combined with the Davies?Bouldin index and Dunn index, the clustering results were analyzed to judge the difference between the two clustering algorithms. The analysis based on the sample data and data characteristics selected in this article revealed that the K-Means algorithm achieved better clustering results than TwoStep. This research can provide a powerful reference for selection among various clustering algorithms in blast furnace ironmaking big data analysis.