| 脱磷转炉脱磷渣FeO预报模型 |
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| 引用本文： | 苏晓伟,崔衡,张丙龙,刘延强,罗磊,季晨曦. 脱磷转炉脱磷渣FeO预报模型[J]. 重庆大学学报(自然科学版), 2018, 41(8): 56-65. DOI: 10.11835/j.issn.1000-582X.2018.08.007 |
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| 作者姓名： | 苏晓伟 崔衡 张丙龙 刘延强 罗磊 季晨曦 |
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| 作者单位： | 北京科技大学 钢铁共性技术协同创新中心,北京,100083首钢京唐钢铁联合有限责任公司,河北唐山,063200首钢技术研究院,北京,100043 |
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| 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。
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| 关 键 词： | 脱磷转炉 预报模型 神经网络 |
| 收稿时间： | 2018-01-02 |
FeO prediction model of dephosphorization slag in converter for dephosphorization |
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| SU Xiaowei,CUI Heng,ZHANG Binglong,LIU Yanqiang,LUO Lei and JI Chenxi. FeO prediction model of dephosphorization slag in converter for dephosphorization[J]. Journal of Chongqing University(Natural Science Edition), 2018, 41(8): 56-65. DOI: 10.11835/j.issn.1000-582X.2018.08.007 |
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| Authors: | SU Xiaowei CUI Heng ZHANG Binglong LIU Yanqiang LUO Lei JI Chenxi |
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| Affiliation: | Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, P. R. China,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 Jingtang United Iron and Steel Co. Ltd., Tangshan 063200, Hebei, P. R. China,Shougang Jingtang United Iron and Steel Co. Ltd., Tangshan 063200, Hebei, P. R. China and Shougang Research Institute of Technology, Beijing 100083, P. R. China |
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| Abstract: | 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. |
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| Keywords: | converter for dephosphorization prediction model neural network |
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