A Refined Prediction Model of Silicon Content Based on the Kalman Filter
会议名称：《第二十九届中国控制会议》
会议日期：2010年
学科分类：080602[工学-钢铁冶金] 08[工学] 0806[工学-冶金工程]
基 金：supported by The Ministry of Science and Technology of the PRC Nation-Class Science and Technology Achievements Promotion Plan Blast Furnace Iron-making Intelligent Control Expert System,National Development and Reform Commission,No.2080 Information on Industrial Automation High-tech Industrialization Plan in 2004 by National Development and Reform Commission Baotou Steel Corporation 6# Blast Furnace Expert System Plan National Natural Science Foundation of China under Grant No.60911130510 Zhejiang Provincial Natural Science Foundation of China under Grant No.Y107110 Research Fund for the Doctoral Program of Higher Education of China(for new teachers)under Grant No.20070335161 the Open Project of State Key Laboratory of Industrial Technology,Zhejiang University,under Grant No.ICT0904 Zhejiang University Youth Seed Foundation for Interdisciplinary Studies in 2009
关 键 词：Silicon Content in Hot Metal Blast Furnace Ironmaking Process Residual Kalman Filter TGARCH
摘 要：Prediction of silicon content in hot metal is an important task in the control of blast furnace ironmaking *** to the complexity of blast furnace ironmaking process,most predictive models may work well under stable conditions,however,when the production is instable,the performance may deteriorate,which means loss of much valuable information contained in the ***,the residuals of the predictive model consist of two parts,i.e.,unmodelled information and *** this paper,a TGARCH model（Threshold autoregressive conditional heteroskedasticity model）is used to predict silicon content in hot metal and the residuals are modeled by a Kalman *** Kalman filter is used to separate the unmodeled information from noise and the captured information is then incorporated into the original *** proposed method was tested on data collected from a medium-sized blast *** results shows that Kalman filter well improve the accuracy of TGARCH model.