基于雷达观测数据的高炉料面多模型控制

炉料在高炉内部的布局直接影响着高炉的运行,好的布料策略能极大地提高生产力,并带来巨大的经济效益.本文利用模糊c均值聚类算法对大量雷达扫描得到的料面数据进行分类,建立多模型料面模型集;设定期望料面,并根据料面模型集中的多种料面,设计多种布料控制策略,求出相应的布料矩阵.每一个布料周期,采用模糊识别的方法把获得的实时料面数据与模型集相匹配,进而采取相应的布料矩阵进行布料,直至达到期望料面.由于雷达扫描数据的存在,形成了反馈机制,使得高炉布料能够实现闭环控制.本控制策略在某钢铁厂2500m3高炉上得到实施,取得很好的控制效果,达到节能降耗的要求.

Multi-model control of blast furnace burden surface based on observed data of radars

The operation of blast furnace is directly affected by the charging distribution. Productivity can be boosted considerably if good charging distribution strategy is adopted. At the same time, great economic benefits will be brought about. In our method, a large amount of burden surface data from radars are classified by using fuzzy c-means clustering, and the multiple models set of burden surface is built. When the expected burden surface is given, multiple control strategies are designed based on multiple burden surfaces of the model set, and multiple charge distribution are obtained. In every charging distribution period, the real time burden surface data will be matched with the model set by fuzzy recognition, and the corresponding charge distribution matrices will be selected for charge distribution until the expected burden surface is produced. Feedback mechanism is formed from the observed data of radars, and closed-loop control is realized. The proposed control strategy is applied to a 2500m3 blast furnace in an Iron and Steel Plant; the control effect has been improved greatly, and the energy conservation and consumption reduction are realized.