摘要
基于实际生产数据分析研究影响钢水成分与温度的因素,通过利用BP神经网络的理论,建立了钢水成分与温度预报模型,实现了提前对钢水成分与温度的预报。钢水成分预报值与实测值基本吻合,钢水温度预报的命中率在84.0%以上,模型具有较高的预报精度。
On the basis of the practical production data,the factors affecting the components and temperature of molten steel were analyzed,and by using BP neural network theory,the molten steel components and temperature forecast model was established,which realized the composition and temperature of molten steel forecast.The difference between forecast value and actual value for the molten steel components is small,and the shooting of the molten steel temperature forecast is 84.0% above,so the model has high precision of forecast.
出处
《工业加热》
CAS
2013年第1期15-17,20,共4页
Industrial Heating
关键词
电弧炉
成分
温度
预报
模型
EAF
components
temperature
forecast
model
