Application of Neural Network to Predict Sulphur Content in Hot Metal
WANG Wei1，CHEN Weilin2，YE Yong2，XU Zhihui2，JIA Bin2
1． Key Laboratory of Ferrous Metallurgy and Resources Utilization, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China; 2. Wuhan Iron and Steel Group Corporation, Wuhan 430082， Hubei, China
Abstract：A model for predicting the sulphur content in hot metal based on neural networks is introduced. Blast temperature, blast flux, top temperature, burden, coal injection rate, sulphur content in ore, sulphur content in coke, sulphur content in coal and silicon content of last tap were selected as inputs. The inputs were treated with time lag to improve prediction. Some methods were adopted to resolve the problem of local convergence and long learning time of BP neural network. The predicted results indicated that the hitting rate was 70.7% when the absolute error was less than 0.001, and the hitting rate was 90% when the absolute error was less than 0.005. Thus the validity of the model was proved.
王炜;陈畏林;叶勇;徐智慧;贾斌. 神经网络在高炉铁水硫含量预报中的应用[J]. 钢铁, 2006, 41(10): 19-0.
WANG Wei;CHEN Weilin;YE Yong;XU Zhihui;JIA Bin. Application of Neural Network to Predict Sulphur Content in Hot Metal. Iron and Steel, 2006, 41(10): 19-0.
2006, Vol. 41 
