Study For Prediction And Control Model Of Silicon Content In B.F Hot Metal Based On Nonparametric Regression

As the main upper procedure of metallurgical industry,Blast Furnace(BF) ironmaking is an important component of steel industry in national economy,which plays a significant role in energy saving and technical development of the whole industry.The ironmaking process is highly complicated,whose operating mechanism is characteristic of nonlinearity,time lag,high dimension,big noise and distribution parameters etc,thus makes it difficult to model the process accurately and effectively.Nonparametric regression is an important part of Nonparametric Statistical Theory.It is widely used in econometrics,traffic system and clinical statistics etc.In nonparametfic regression,the form of regression function is discretional,and there is little restriction on the form of regression function and the distribution of independent and dependent variables,which well accommodates the problem of deviation between model assumptions and real data.The current work uses data collected from BF No.6(2500 m~3) in Baotou Iron & Steel Group Co.to identify the model.The autocorrelation of[Si]series was analyzed and strong correlation was detected.Correlation coefficients and gray relation entropy between monitored process variables and silicon content in hot metal were also discussed.Section 4 deals with the problem of dimension reduction of model parameters based on partial least squares(PLS).By performing PLS redundancy is reduced and the most useful information in input variables is extracted to reflect the fluctuation of silicon content.A generalized additive model(GAM),which gets the local approximated function relation via an iterative process of nonparametric smooth function,was constructed using the three variables selected from PLS to predict the silicon content.On the basis of above analysis,section 5 uses genetic algorithms and orthogonal sequence estimation method to find the best parameters combination to indicate the fluctuation of silicon content.Simulation results prove the correctness of the previous analysis on relations between process variables and silicon content.A optimal nonparametric regression model for prediction of silicon content was constructed and good result was obtained.By Combined the nonparametric regression and the hybrid control partial differential function of BF ironmaking,a varying-coefficient regression model for predictive control of blast furnace hot metal temperature was given in section 6.It analyses the local linear connection between parameters like speed of materials LS, wind blasted FQ,coal injected PM and the permeability index FF and the output variable silicon content.A weight matrix is used to describe the influence that each parameter to silicon content in hot metal.Thus a theoretic basis of predictive control is established.