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铝电解槽槽况综合分析方法研究 被引量:2

Research on the comprehensive analysis for the aluminium reduction cell
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摘要 铝电解槽槽况工艺参数中所存在的隐藏信息,是判断电解槽运行的重要依据。研究了铝电解槽槽况分析的方法——离散度分析和聚类分析方法;采用离散度分析方法对电解槽热量平衡和物料平衡进行了分析;利用聚类分析方法对电解槽槽况进行了分类。实例表明,聚类分析和离散度分析能够有效地诊断槽况,为铝电解生产提供参考。 A lot of useful information,which hide in the parameters of the aluminium reduction cell,are the important criterions to assess the situation of the cell.Two methods,including the dispersion analysis and the cluster analysis,are presented to analyze the situation of aluminium cell.The thermal and material balances for the aluminium cell are investigated using the analysis of dispersion and the situations of the cells are classified through the cluster analysis.The results showed that the cluster analysis and the dispersion analysis can validly determine the situations of the cell,and provide some useful references for the aluminium productions.
出处 《轻金属》 CSCD 北大核心 2011年第10期38-42,共5页 Light Metals
基金 青海大学华鼎大学生科技创新基金项目(HD-200908)
关键词 铝电解 离散度分析 数据挖掘 聚类分析 aluminium reduction analysis of dispersion data mining cluster analysis
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  • 1李劼,王志刚,赖延清,刘伟,徐宇杰.5kA惰性阳极铝电解槽槽膛内形及热平衡[J].过程工程学报,2008,8(S1):54-58. 被引量:10
  • 2吴连成,马志军.大型预焙铝电解槽阳极电热场的计算分析[J].中国有色金属,2007(5):74-75. 被引量:3
  • 3M S Chen,J Han,P S Yu.Data mining:An overview from a database perspective[J].IEEE Trans Knowledge and Data Engineering,1996;8(6):866~883
  • 4R Ng,J Han.Efficient and effective clustering method for spatial data mining[C].In:Proc 1994 Int Conf on Very Large Databases(VLDB94),Santiago,Chile,1994:144~155
  • 5T Zhang,R Ramakrishnan,M Livny.BIRCH:an efficient data clustering method for very large databases[C].In:Proc ACM-SIGMOD Int Conf Management of Data (SIGMOD'96),Montreal,Canada,1996-06:103~114
  • 6S Guha,R Rastogi,K Shim.Cure:An efficient clustering algorithm for large databases[C].In:Proc 1998 ACM-SIGMOD Int conf Managementof Data(SIGMOD'98),Seattle,.WA,USA,1998:73~84
  • 7S Guha,R Rastogi,K Shim.ROCK:a robust clustering algorithm for categorical attributes[C].In:Proc of the 15th Int Conf on Data Engineering(ICDE'99),Sydeny,Australia,1999-03:512~521
  • 8G Karypis,E Han,V Kumar.Chameleon:Hierarchical Clustering Using Dynamic Modeling[J].IEEE Computer,1999;32 ( 8 ):68~75
  • 9M Ester,H P Kriegel,J Sander et al.A density-based algorithm for discovering clusters in large spatial databases with Noise[C].In:Proc Int Symp on Large Spatial Databases(SSD'95),1995:67~82
  • 10M Ankerst,M Breunig,H P Kriegel et al.Optics:Ordering points to identify the clustering structure[C].In:Proc 1999 ACM-SIGMOD Int conf Management of Data (SIGMOD'99),Philadelphia,PA,1999-06:49~60

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