Establishment of Neural Network Prediction Model for Terminative Temperature Based on Grey Theory in Hot Metal Pretreatment

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• 作者：Hui-ning ZHANG^a ; ^b ; ^{zhanghuining2008@126.com}Author Vitae ; An-jun XU^a ; ^b ; ^{anjunxu@126.com} ; Jian CUI^c ; Dong-feng HE^a ; ^b ; Nai-yuan TIAN^a ; ^b
• 关键词：grey theory ; correlation degree ; dephosphorization ; terminative temperature ; neural network model
• 刊名：Journal of Iron and Steel Research, International
• 出版年：2012
• 出版时间：June, 2012
• 年：2012
• 卷：19
• 期：6
• 页码：25-29
• 全文大小：1K

文摘

In order to improve the accuracy of model for terminative temperature in steelmaking, it is necessary to predict and control before decarburization. Thus, an optimization neural network model of terminative temperature in the process of dephosphorization by laying correlative degree weights to all input factors related was used. Then simulation experiment of model newly established is conducted utilizing 210 data from a domestic steel plant. The results show that hit rate arrives at 56.45 % when error is within plus or minus 5 % , and the value is 100 % when within ¡À10 % . Comparing to the traditional neural network prediction model, the accuracy almost increases by 6.839 % . Thus, the simulation prediction fits the real perfectly, which accounts for that neural network model for terminative temperature based on grey theory can reflect accurately the practice in dephosphorization. Naturally, this method is effective and practicable.