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基于PSO优化SVM的转炉炼钢用氧量预测研究
引用本文:秦波,吴庆朝,张娟娟,王建国,张文兴.基于PSO优化SVM的转炉炼钢用氧量预测研究[J].测控技术,2014,33(12):121-124.
作者姓名:秦波  吴庆朝  张娟娟  王建国  张文兴
作者单位:1. 内蒙古科技大学机械工程学院,内蒙古包头,014010
2. 中冶东方包头钢铁设计研究院,内蒙古包头,014010
基金项目:内蒙古自然科学基金重大项目(2011ZD08)
摘    要:用氧量是影响钢水质量的主要因素之一,为提高转炉炼钢用氧量模型的预测精度,提出基于PSO优化SVM的吹氧量建模预测方法。针对SVM结构参数依据经验选取,致使预测模型的泛化能力差,在标准PSO算法的基础上,优化SVM的惩罚系数、不敏感损失系数和高斯核宽度系数3个结构参数,并建立转炉炼钢用氧量预测模型;在此基础上利用UCI数据库中的Auto-MPG标准数据,验证了方法的有效性;最后以某钢厂100 t转炉的实际生产数据建立吹氧量预测模型,结果表明,与标准BP、RBF及SVM相比,基于PSO优化SVM的转炉炼钢吹氧量预测模型精度高、泛化能力强。

关 键 词:PSO优化SVM  用氧量预测  氧气吹顶转炉炼钢

Blowing Oxygen Volume Prediction of BOF Steelmaking Based on PSO-SVM
QIN Bo , WU Qing-chao , ZHANG Juan-juan , WANG Jian-guo , ZHANG Wen-xing.Blowing Oxygen Volume Prediction of BOF Steelmaking Based on PSO-SVM[J].Measurement & Control Technology,2014,33(12):121-124.
Authors:QIN Bo  WU Qing-chao  ZHANG Juan-juan  WANG Jian-guo  ZHANG Wen-xing
Abstract:The blowing oxygen volume is one of the main factors affecting the quality of molten steel.To improve the prediction accuracy of blowing oxygen volume model of converter steelmaking,an blowing oxygen prediction model based on PSO optimized SVM is proposed.Due to the structural parameters of the SVM are commonly selected according to human experience,the generalization ability of the prediction model is not satisfactory.Aiming at this problem,standard PSO algorithm is used to optimize the three critical structure parameters of the SVM,namely punish coefficient,loss coefficient and Gaussian kernel width coefficient.Converter steelmaking with oxygen prediction model is established.Then,the standard Auto-MPG data from UCI database and a set of practical data collected from a certain steel plant in China are used to build the oxygen blowing prediction model especially.Both experimental results show that the PSO optimized SVM oxygen blowing prediction model has a high precision and strong generalization ability compared with the standard BP,RBF and SVM.
Keywords:PSO-SVM  prediction of blowing oxygen volume  basic oxygen furnace(BOF) steelmaking
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