Abstract
This paper discusses the rapid development in computer technology and neural networks that are used to transform recently developed concepts and available technology into a new generation of intelligent automation systems. In this study features extracted from images of froths by an on-line machine vision system in an industrial precious metal flotation plant were used to relate froth characteristics with the performance of the plant by using self-organising and Sammon maps. This intelligent vision system constitutes a powerful tool for the investigation and interpretation of the effect of various flotation parameters. Previous work is extended by relating surface froth characteristics with industrial flotation control and performance variables. This method of system identification represents a significant development towards an automatic control system.
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