脱磷转炉脱磷渣FeO预报模型  

FeO prediction model of dephosphorization slag in converter for dephosphorization

作　　者：苏晓伟 崔衡 张丙龙[2] 刘延强[2] 罗磊[2] 季晨曦[3] SU Xiaowei;OUI Heng;ZHANG Binglong;LIU Yanqiang;LUO Lei;JI Chenxi(Collaborative Innovation Center of Steel Technology,University of Science and Technology Beijing,Beijing 100083,P.R.China;Shougang Jingtang United Iron and Steel Co.Ltd.,Tangshan 063200,Hebei,P.R.China;Shougang Research Institute of Technology,Beijing 100083,P.R.China)

机构地区：[1]北京科技大学钢铁共性技术协同创新中心,北京100083 [2]首钢京唐钢铁联合有限责任公司,河北唐山063200 [3]首钢技术研究院,北京100043

出　　处：《重庆大学学报（自然科学版）》2018年第8期56-65,共10页Journal of Chongqing University

摘　　要：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。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, A12O3） 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.

关 键 词：脱磷转炉 预报模型 神经网络 

分 类 号：TF703.6[冶金工程—钢铁冶金]