Characterization of flotation processes with self-organizing neural nets

Flotation processes are difficult to describe fundamentally, owing to the stochastic nature of the froth structures and the ill-defined chemorheology of the froth. Considerable information on the process is reflected by the structure of the froth. In previous work it has been shown that structural features extracted from flotation froths can be related to the behavior of flotation processes in a qualitative way through the identification of certain behavioral regimes or classes by using a supervised neural net as classifier. Although useful as an aid to control decisions, this method is less suitable for quantitative or dynamic analysis of the behavior of flotation plants. In this paper a new method for the analysis of flotation plants is consequently proposed, based on the use of order preserving maps of features extracted from digitized images of the froth phase. The construction of these maps by means of a self-organizing neural net is demonstrated by way of examples concerning the analysis of industrial copper and platinum flotation plants.