The Monitoring of Mineral Processing Operations Using Computer Vision and Neural Networks

In the minerals industry numerous problems cannot be solved by conventional mathematical models owing to their complexity or a lack of phenomenological understanding. Neural networks provide one way of mapping the ill-defined relations between process variables and functions for such ill-defined problems. Consequently, processes such as leaching and froth flotation are mostly controlled in an empirical way by using rules of thumb. In addition, these processes involve so many independent and dependent variables that the plant operator finds it difficult to visualise or even observe a change in process conditions. The structure of froths developed on the surfaces of industrial scale froth flotation cells has a significant effect on both the grade and recovery of valuable minerals in the concentrate. Although these effects are well known at the process operational level, where considerable heuristic knowledge is available, little work has been reported on a detailed characterisation of the mechanisms and the visual characteristics of the surface froth. Recent results from an on-line observation of froths in several plants have proved the relationships between image features representing froth behaviour and metallurgical results. It will be shown how supervised and unsupervised neural nets are being used on operating plants to interpret computer vision data. In froth flotation the operator is supposed to visually observe process changes from the appearance of the froth, which is an unreasonable demand under industrial conditions. The system described here determines textural parameters on-line, and tracks the changes in process conditions via a Self-Organising Map (SOM) neural net. This monitoring system warns the operator about fluctuations in reagent addition, and gives an idea of the type of froth encountered. In another example, changes in the mineralogical characteristics of gold ores are represented on an SOM map, based on the diagnostic leaching behaviour of such ores.