This paper summarises research into the study of the control of flotation processes based on froth images. A review of the froth flotation operation background and the relevance and role of the froth phase are presented. The use of numerical image analysis techniques for the extraction of significant patterns from industrial flotation froths is discussed.
First, the froth was analysed by the use of the multivariate image analysis (MIA) method to extract the spectral variation contained in a RBG image. The partial least squares (PLS) method was applied to develop an empirical model for the prediction of froth grade. The results of this application are illustrated using an industrial case study.
Second, the froth was also analysed by grey-level co-occurrence matrix (GLCM) and wavelet transform analysis (WTA) to extract features related more to morphological aspects. Since there is useful information hidden in froth texture, these methods are used as texture descriptor to classify different types of froths. They also provide qualitative information on the changes in the visual appearance of the froth. The pros and the cons of those methods are highlighted.
Third, a control strategy based on a froth structure indicator was developed and implemented on an industrial zinc flotation circuit. The feedback controller uses the reagent flow-rate in order to maintain the froth structure at its set point. Process improvements obtained from the implemented control strategy are highlighted.
Finally, the feasibility of implementing a hybrid control strategy consisting in a froth structure supervision controller based on the froth concentrate grade estimated by MIA and recovery inferred by image analysis is discussed.