脱磷转炉脱磷渣FeO预报模型

为提高"全三脱"工艺脱磷转炉的脱磷效率、降低钢铁料的消耗,基于氧平衡机理模型,采用Levenberg-Marquardt神经网络优化算法,建立了脱磷转炉脱磷渣FeO预报模型。将氧平衡机理模型计算的氧化物(FeO,CaO,SiO_2,MgO,MnO,P_2O_5,Al_2O_3)质量和出钢温度作为输入项导入神经网络工具箱,训练成误差最小化的网络。结果表明,FeO预测值与实测值相对误差在10%以内的炉次达到85%。建立的模型具有较高的预报命中率,可为现场生产提供理论依据。

FeO prediction model of dephosphorization slag in converter for dephosphorization

In order to reduce the iron loss and improve the dephosphorization efficiency of the converter for dephosphorization by the full triple stripping process, a model, based on the oxygen balance mechanism, is bulit to predict the end point FeO content and the Levenberg-Marquardt neural network algorithm is adopted in this model. The calculation of the oxide mass (FeO, CaO, SiO₂, MgO, MnO, P₂O₅, Al₂O₃) with the oxide balance mechanism model and the tapping temperature are used as inputs to the neural network toolbox to train the network with minimum error. The results show that the heat with relative error of 10% between the predicted value and the measured value of FeO is up to 85%.This proves that the FeO prediction hit rate of the model is high, and can provide theoretical basis for production on site.