Prediction of Si Content in Blast Furnace Based on Fuzzy Model

摘要：

According to the characteristics of massive input and output data, complicated production process and the difficulty to acquire accurate mathematical model of the non-linear blast furnace system, this paper proposes a fuzzy system modeling method based on data driving. This paper utilizes the fuzzy clustering algorithms combined nearest neighbor clustering and fuzzy c-means clustering to classify the input space. And then the parameters of model are identified by adopting recursive least square algorithm. Subsequently, the fuzzy rules are extracted to build the T-S fuzzy model of blast furnace system whose output is the Si content in molten iron which has guiding significance on predicting the trend of furnace temperature changing. The furnace data of number 6 furnace in Baotou Steel is acquired to complete MATLAB simulation. The simulation results verify the feasibility of the method.

关键词： Si Content in Molten Iron T-S Fuzzy Model Fuzzy Clustering Recursive Least Square Algorithm

作者: CUI Guimei LIU Min MA Xiang ZHANG Yong

作者单位: Inner Mongolia university of science and technology information engineering institute, Inner Mongoli Baotou steel group company, Inner Mongolia Baotou 014010

会议类型: 国际会议

会议名称: The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

会议地点: 太原

会议语种:英文

页码: 3187-3191

在线出版日期: 2012-05-23（万方平台首次上网日期，不代表论文的发表时间）