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.