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

查看全文 作　　者：Fei [1,2]HE;Dong-feng [1,2]HE;An-jun [1,2]XU;Hong-bing [3]WANG;Nai-yuan [1,2]TIAN 高影响力作者 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高影响力机构 出　　处：《Journal of Iron and Steel Research(International)》索引2014年第21卷第2期,共10页高影响力期刊 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 status and artificial neural network has been proposed to predict molten steel temperature.The hybrid model could overcome 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 established 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 temperature,calculates target end temperature in LF,target starting temperature in LF,target temperature in argon-blowing 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：人工神经网络 混合模型 温度预测 热状态 钢包 中间包温度 炼钢过程 钢水温度
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