THE INTERPRETATION OF FLOTATION FROTH SURFACES BY USING DIGITAL IMAGE-ANALYSIS AND NEURAL NETWORKS

The rapid developments in computer vision, computational resources and artificial intelligence, and the integration of these technologies ar e creating new possibilities in the design and implementation of comme rcial machine vision systems. In chemical and minerals engineering, nu merous opportunities for the application of these systems exist, of wh ich the characterization of flotation froth structures is a good examp le of the utilization of visual data as a supplement to conventional p lant data. In this paper images from pyrite batch flotation tests cond ucted after a factorial design as well as images from a copper flotati on plant were used to understand the relationship between froth charac teristics and flotation performance better. The results show that a si gnificant amount of data can be extracted from flotation surface froth s. Techniques have been developed to characterize chromatic informatio n, average bubble size, froth texture, froth stability and mobility of surface froths. It has been shown that most of the froth characterist ics of this study can be explained in terms of the concentration of so lids in the froth and the factors that affect the solids concentration . The techniques developed proved to be useful in investigating the ef fect of a mixed collector and the addition of copper sulphate. The dep ressing effect of the copper sulphate and the higher grades and recove ries made possible by the mixed collector under these conditions were explained by analysis of the froth features. Excellent results were ob tained in modelling the relation between froth characteristics or frot h grade and recovery by using a backpropagation neural network. A sens itivity analysis showed that the most important froth features for the experimental conditions of this study are the froth stability, mobili ty and average bubble size. This computer vision system constitutes a powerful research tool for the investigation and interpretation of the effect of various flotation parameters. This paper also shows how the rapid development in computer technology and related disciplines can be used to transform recently developed concepts and available technol ogy into a new generation of intelligent automation systems.