【摘 要】
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Our research concerns about basic oxygen furnace(BOF)steelmaking process,according to the analysis of actual industry data,the soft measure modeling method is proposed for BOF steelmaking process.The
【机 构】
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Institute of Industrial Engineering and Logistics Optimization,Northeastern University,Shenyang,1108
论文部分内容阅读
Our research concerns about basic oxygen furnace(BOF)steelmaking process,according to the analysis of actual industry data,the soft measure modeling method is proposed for BOF steelmaking process.The key idea of the proposed method is to illustrate the end-point prediction in the BOF steelmaking process.In order to predict the end-point temperature and the end-point carbon content more accurately in BOF steelmaking process,combining modified particle swarm optimization with least squares support vector machine(MPSO-LSSVM)approach is utilized to establish prediction models,MPSO is used to modify the parameters of the model,so that the model can have a better adaptability.At the same time,this paper adopts the idea based on the event,so as to strength the universal capability of model.Finally,experimental results indicate that the soft measure prediction method is effective; and it can successfully apply to the actual industry field.
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