Abstract
In many industries, including the mineral processing industry, process modelling can be improved by mining the data historian. However, the data in the historian is often contaminated with missing values, unknown operating conditions, and other imperfections. Furthermore, manual segmentation of the data is difficult due to the large number of data points and variables. Thus, there is a need to develop and implement methods that can automatically segment the data set into viable components for identification purposes. One approach uses Laguerre models to segment the data set. However, when used in a multivariate situation, such as in the zinc flotation cell, various issues, such as collinearity, arise. Therefore, the data segmentation algorithm needs to take this into consideration when examining a data set. Using the zinc flotation cell, it is shown that for the multivariate case preselecting the data variables to consider improves the data segmentation.
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