采用3层BP神经网络来预报高炉铁水硫含量，根据高炉冶炼的实际生产数据，选取风温、风量、炉顶温度、焦炭负荷、喷煤量、矿石硫含量、焦炭硫含量、煤粉硫含量和上一炉铁水硅含量9个因素作为输入变量，为提高神经网络预报的准确率，对输入参数进行时滞处理。采取附加动量项和自适应学习步长的措施，解决了BP神经网络局部收敛和学习时间过长的问题。模型预报结果表明：当允许绝对误差不大于0.001时，预报命中率为70.7%；当允许绝对误差不大于0.005时，命中率为90%，证明了模型的有效性。
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.
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