Abstract
Today, massive amounts of data are being collected routinely on hundred of variables at high frequency. However, many important variables, such as concentrate and tailings grades are available only infrequently in flotation processes. To effectively monitor flotation columns one must utilize all the information contained in routine measurements. The main difficulties are the dimensionality and the fact that process operating variables are often highly correlated and collinear. Multivariate statistical projection methods have been proposed to treat these situations. These ideas are demonstrated using simulation studies on flotation columns.
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