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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 ±10%.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.
In order to improve the accuracy of model for terminative temperature in steelmaking, it is necessary to predict and control before decarburization .hus, 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 the simulation experiment of model newly established is utilized utilizing 210 data from a domestic steel plant. The results show that hit rate at 56.45% when error is within plus or minus 5%, and the value is 100% when within ± 10 The accuracy of the prediction neural network model for terminations temperature based on gray theory can reflect accurately the practice in dephosphorization .Naturally, this method is effective and practicable.