Abstract
In this study image features extracted from froth images by an on-line machine vision system in an industrial platinum flotation plant were used to relate froth characteristics and flotation performance by using neural networks. It has been shown that a considerable amount of data can be extracted from flotation surface froths and that both novel feedback control procedures and feedback control as a complement to conventional feedforward systems are made possible. Feature measures such as chromatic information, average bubble size, froth texture, froth stability and mobility of surface froths were used in the on-line classification of flotation froths. This intelligent vision system constitutes a powerful research tool for the investigation and interpretation of the effect of various flotation parameters. This paper shows how the rapid development in computer technology and neural networks can be used to transform recently developed concepts and available technology into a new generation of intelligent automation systems.
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