（1. College of Materials and Metallurgy, Guizhou University, Guiyang 550003, Guizhou, China 2. Jiangsu Shagang Group Co., Ltd., Zhangjiagang 215625, Jiangsu, China
Abstract：Through studying the main influencing factor of hot metal transportation process temperature for BF-BOF interface, the main parameters affecting temperature of hot metal transportation process for BF-BOF interface was determined, and a prediction model of hot metal temperature for BF-BOF interface was established based on Levenberg-Marquardt (LM) algorithm of BP neural network. The data of 100 ladles were used to training the model and the other 50 ladles were selected as the predictive samples. It is shown that: under the model of “one hot metal ladle going through process” for BF-BOF interface, when the absolute error│X│≤20℃, the temperature of hot metal is shooting 94%, the hit rate of temperature drop of hot metal is 78%; when the absolute error│X│≤40℃, the temperature of hot metal is shooting 100%, the hit rate of temperature drop of hot metal is 92%, this prediction model can meet the actual production needs and can provide a very good guide to steel-making production.
收稿日期: 2012-01-10
出版日期: 2012-10-17
引用本文:
任彦军,，王家伟，张晓兵，赵浩文. 基于LM算法BP神经网络的高炉-转炉界面铁水温度预报模型[J]. 钢铁, 2012, 47(9): 40-42.
kREN Yan-jun1，2，WANG Jia-wei1，ZHANG Xiao-bing2，ZHAO Hao-wen1. Prediction Model of Hot Meltal Temperature for BF-BOF Interface Based on LM BP Neural Networ. Iron and Steel, 2012, 47(9): 40-42.
2012, Vol. 47 
