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

doi:10.1016/S1474-6670(17)37020-9

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

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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 language	English
Pages (from-to)	357-362
Journal	IFAC PROCEEDINGS VOLUMES
Volume	33
Issue number	22
DOIs	https://doi.org/10.1016/S1474-6670(17)37020-9
Publication status	Published - 2000
MoE publication type	A4 Conference publication
Event	IFAC Workshop on Future Trends in Automation of the Mineral and Metal Processing - Espoo, Finland Duration: 22 Aug 2000 → 24 Aug 2000

Keywords

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

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using principal component analysisSubmitted manuscript, 2.05 MB

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

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.",

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KW - self-organizing map

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

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Keywords

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