Intelligent prediction method for blast furnace condition based on information granules analysis of temperature in cooling stave
The smooth condition of the blast furnace is important for the production and quality of the hot metal.The stability of the slag crust indicates the stability of the conditions in the blast furnace,and the temperature in the cooling stave can describe the stability of the slag crust.For predicting the conditions of the blast furnace according to the dynamic features of the temperature in the cooling stave,this study presented an intelligent method for predicting the conditions of the blast furnace based on the information granules of the temperature in the cooling stave.Firstly,the Spearman corre-lation analysis method is employed to select the parameters that affect the dynamic features of the temperature in the cooling stave.Secondly,the information granulation method is used to extract the dynamic features of the selected parameters and represent the data in a granular form.Then,the pre-diction model of the information granule of temperature in the cooling stave is built based on the sup-port vector regression with the inputs of information granules of the selected parameters,realizing the prediction of the temperature in the cooling stave.Finally,based on the predicted information gran-ules,the conditions of the blast furnace can be recognized using a condition prediction method.Ex-periments conducted using actual steel enterprise data show that the presented method can predict the conditions in the blast furnace and provides powerful guidance for operators to make a proper burden distribution decision-making strategy.
blast furnacecondition predictiontemperature in cooling staveinformation granuletime senes
NETL
NSTL
万方数据
