RH精炼终点预报模型

The end point prediction model for RH refining

为了提高RH精炼钢液温度和成分命中率和控制精度,综合RH深脱碳冶金机理模型和NARX神经网络温度模型的优点,利用Visual Basic 6.0语言结合Matlab神经网络工具箱,开发了RH精炼终点温度和成分预报模型,能够计算RH精炼吹氧量和合金加入量,并对RH精炼终点钢液温度和成分进行离线预报。模型预报精度较高,温度和成分（同时命中）的平均命中率达到85%,温度误差在±5℃以内的比例达到90%,碳质量分数预报误差均在5×10-6以内,Si,Mn,P,Als含量的平均命中率（相对误差在±5%以内的比例）均在90%以上;吹氧量、低碳硅铁、磷铁、铝粒和微碳锰铁加入量预报误差在-3%~7%以内的比例分别为90%、75%、75%、95%和70%。

In order to improve the hit rate and accuracy of temperature and composition of molten steel during RH refining process,the prediction model for the end point temperature and composition of molten steel was established based on integrating of metallurgical mechanism and NARX Neural network.The prediction model program that was able to calculate the amount of oxygen and alloy and to predict the end point was developed by utilizing of Visual Basic 6.0and Matlab neural network toolbox.The model showed high prediction accuracy with over 85 % hit rate of temperature and composition at the same time.The rate of temperature errors less than 5 ℃reached 90 % and all the carbon mass fraction prediction errors were within 5×10-6.The rates of Si,Mn,P and Alsprediction errors less than±5 %exceeded 90 %.In addition,the rates of oxygen,low-carbon ferrosilicon,iron phosphate,aluminum and micro-carbon ferromanganese amount prediction error within-3 %to 7 %were 90 %,75 %,75 %,95 %and 70 %,respectively.