模糊C均值聚类算法在高炉料面分类中的应用  

Application of the fuzzy C-means clustering algorithm in blast furnace burden surface identification

作　　者：刘德馨[1,2] 李晓理[1,2] 周翔[1,2] 陈先中[1,2] 尹怡欣[1,2] 侯庆文[1,2] 

机构地区：[1]北京科技大学自动化学院,北京100083 [2]北京科技大学钢铁流程先进控制教育部重点实验室,北京100083

出　　处：《北京科技大学学报》2012年第6期683-690,共8页Journal of University of Science and Technology Beijing

基　　金：国家高技术研究发展计划资助项目(2009AA04Z156);国家自然科学基金资助项目(61074055);北京市优秀人才培养资助项目(2009D013001000016);北京市教委共建重点学科资助项目(xk100080537)

摘　　要：通过对多雷达扫描得到的高炉料面进行数据处理,根据数据的特征,分别采用模糊C均值聚类和特征加权模糊C均值聚类算法对料面数据进行分类,建立标准料面模型库.再通过模糊模式识别中贴近度的方法把待分类的目标料面与模型库相匹配,为后续的布料控制提供依据.该算法在某2500 m3高炉上进行了实验,取得良好的效果.仿真结果表明了其有效性.Blast furnace burden surface data derived from multi radars were processed. Fuzzy C-means and feature weighted fuzzy C-means clustering were applied to identify the burden surface data according to the data information, and a standard burden surface model database was set up. Each target burden surface was matched with the model database by using the method of nearness in fuzzy pattern recognition, and this provides a basis for the next burden surface control. The algorithm was carried out into a 2 500 m3 blast furnace, and the control effect has been improved. The simulation results show the effectiveness of the proposed method.

关 键 词：高炉 布料 模糊聚类 聚类算法 模式识别 

分 类 号：TF325.3[冶金工程—冶金机械及自动化] TP273.4[自动化与计算机技术—检测技术与自动化装置]