首页 | 本学科首页   官方微博 | 高级检索  
     

基于支持向量机的高炉向凉、向热炉况预测
引用本文:崔桂梅,鄢常亮,关英辉.基于支持向量机的高炉向凉、向热炉况预测[J].钢铁研究学报,2011,23(7):18-22.
作者姓名:崔桂梅  鄢常亮  关英辉
作者单位:内蒙古科技大学信息工程学院,内蒙古包头,014010
基金项目:教育部“春晖计划”课题; 内蒙古自然科学基金资助项目(2009MS0911)
摘    要:高炉冶炼过程中炉温是影响技术经济指标的关键参数,保持合理的炉温是高炉稳定顺行的关键因素。采用某炼铁厂在线采集的数据,通过核主元分析对建模数据进行预处理,根据相关系数选定模型参数,确定参数对炉温的滞后时间,基于支持向量机建立了高炉向凉、向热预测诊断模型。通过实例验证,该模型具有很高的精度。

关 键 词:高炉  支持向量机  核主元分析  炉温预测

Prediction of Furnace Status as to Tendency to Cold and Hot Based on Support Vector Machine
CUI Gui-mei,YAN Chang-liang,GUAN Ying-hui.Prediction of Furnace Status as to Tendency to Cold and Hot Based on Support Vector Machine[J].Journal of Iron and Steel Research,2011,23(7):18-22.
Authors:CUI Gui-mei  YAN Chang-liang  GUAN Ying-hui
Affiliation:CUI Gui-mei,YAN Chang-liang,GUAN Ying-hui(School of Information Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,Inner Mongolia,China)
Abstract:Furnace temperature in iron making process was key parameter,which directly affected the primary technique and economics index.One of the key factors for smooth running of blast furnace was to maintain a reasonable temperature.Based on data collected from some ironmaking plant,the model parameters were adopted according to correlation coefficients.Considering the lag time of furnace temperature,status diagnosis system in blast furnaces was proposed based on support vector machines(SVM).In order to obtain pr...
Keywords:blast furnace  SVM  KPCA  prediction of furnace temperature  
本文献已被 CNKI 万方数据 等数据库收录!
点击此处可从《钢铁研究学报》浏览原始摘要信息
点击此处可从《钢铁研究学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号