Hybrid Model of Molten Steel Temperature Prediction Based on Ladle Heat Status and Artificial Neural Network  

作　　者：Fei HE Dong-feng HE An-jun XU Hong-bing WANG Nai-yuan TIAN 

机构地区：[1]School of Metallurgical and Ecological Engineering,University of Science and Technology Beijing [2]State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing [3]School of Computer and Communication Engineering,University of Science and Technology Beijing

出　　处：《Journal of Iron and Steel Research International》2014年第2期181-190,共10页

基　　金：Item Sponsored by Fundamental Research Funds for Central Universities of China(FRF-BR-10-027B)

摘　　要：Aiming at the characteristics of the practical steelmaking process, a hybrid model based on ladle heat sta- tus and artificial neural network has been proposed to predict molten steel temperature. The hybrid model could over- come the difficulty of accurate prediction using a single mathematical model, and solve the problem of lacking the consideration of the influence of ladle heat status on the steel temperature in an intelligent model. By using the hybrid model method, forward and backward prediction models for molten steel temperature in steelmaking process are es- tablished and are used in a steelmaking plant. The forward model, starting from the end-point of BOF, predicts the temperature in argon-blowing station, starting temperature in LF, end temperature in LF and tundish temperature forwards, with the production process evolving. The backward model, starting from the required tundish tempera- ture, calculates target end temperature in LF, target starting temperature in LF, target temperature in argon-blo- wiag station and target BOF end-point temperature backwards. Actual application results show that the models have better prediction accuracy and are satisfying for the process of practical production.Aiming at the characteristics of the practical steelmaking process, a hybrid model based on ladle heat sta- tus and artificial neural network has been proposed to predict molten steel temperature. The hybrid model could over- come the difficulty of accurate prediction using a single mathematical model, and solve the problem of lacking the consideration of the influence of ladle heat status on the steel temperature in an intelligent model. By using the hybrid model method, forward and backward prediction models for molten steel temperature in steelmaking process are es- tablished and are used in a steelmaking plant. The forward model, starting from the end-point of BOF, predicts the temperature in argon-blowing station, starting temperature in LF, end temperature in LF and tundish temperature forwards, with the production process evolving. The backward model, starting from the required tundish tempera- ture, calculates target end temperature in LF, target starting temperature in LF, target temperature in argon-blo- wiag station and target BOF end-point temperature backwards. Actual application results show that the models have better prediction accuracy and are satisfying for the process of practical production.

关 键 词：steelmaking process hybrid model ladle heat status neural network molten steel temperature prediction 

分 类 号：TF701[冶金工程—钢铁冶金]