Abstract：Predesulfurization of hot metal has become an important task for clean steel production. The final sulfur content is a key index of desulfurization station for capacity and efficiency evaluation. Based on the practice of CaO+Mg powder coinjection at Meishan Steel, and improved BP algorithm with new method of adjusting study rate and learning method of maximal error,a prediction model of final sulfur content for hot metal pretreatment was established. The data of 1154 heats were used to training the model and the other 100 heats were selected as the test samples. It was shown that, the improved BP algorithm is more accurate than the normal one, the accuracy of prediction with error less than 0.003% was increased by 28%; for 19 percent of the total test heats the predicted values were the same as the actual ones,90 percent heats were with error less than 0.003%, the average error was 0.0017%.Thus the improved BP algorithm is suitable to predict the final sulfur content for hot metal predesulfurization.
张慧书;战东平;姜周华. 基于改进BP神经网络的铁水预处理终点硫含量预报模型[J]. 钢铁, 2007, 42(3): 30-0.
ZHANG Huishu;ZHAN Dongping;JIANG Zhouhua. Final Sulfur Content Prediction Model Based on Improved BP Artificial Neural Network for Hot Metal Pretreatment. Iron and Steel, 2007, 42(3): 30-0.
2007, Vol. 42 
