Using principal component analysis and self-organizing map to estimate the physical quality of cathode copper

A. Rantala, H. Virtanen, Kari Saloheimo, S-L Jämsä-Jounela

    Research output: Contribution to journalConference articleScientificpeer-review

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    Abstract

    The growing interest in utilising multivariable statistical dimension reduction techniques, PCA and PLS, and neural networks in process monitoring and analysis has resulted in a number of successful industrial applications. This paper describes a process study on the effects of the chemical quality of the anodes on the physical quality of produced cathodes at a copper electrorefining plant through PCA, SOM and a combination of these two techniques. The clustering of anode analysis data over time was compared with the physical quality data of the cathodes.
    Original languageEnglish
    Pages (from-to)357-362
    JournalIFAC PROCEEDINGS VOLUMES
    Volume33
    Issue number22
    DOIs
    Publication statusPublished - 2000
    MoE publication typeA4 Article in a conference publication
    EventIFAC Workshop on Future Trends in Automation of the Mineral and Metal Processing - Espoo, Finland
    Duration: 22 Aug 200024 Aug 2000

    Keywords

    • copper
    • data mining
    • principal component analysis
    • process monitoring
    • refining
    • self-organizing map
    • hybrid method

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