The Development of a Methodology for Automated Sorting In the Minerals Industry

Abstract

The objective of this research project was to develop a methodology to establish the potential of automated sorting for a minerals application. Such methodologies, have been developed for testwork in many established mineral processing disciplines. These techniques ensure that data is reproducible and that testing can be undertaken in a quick and efficient manner. Due to the relatively recent development of automated sorters as a mineral processing technique, such guidelines have yet to be established.

The methodology developed was applied to two practical applications including the separation of a Ni/Cu sulphide ore. This experimentation also highlighted the advantages of multi-sensor sorting and illustrated a means by which sorters can be used as multi-output machines; generating a number of tailored concentrates for down-stream processing. This is in contrast to the traditional view of sorters as a simple binary, concentrate/waste pre-concentration technique.

A further key result of the research was the emulation of expert-based training using unsupervised clustering techniques and neural networks for colour quantisation. These techniques add flexibility and value to sorters in the minerals industry as they do not require a trained expert and so allow machines to be optimised by mine operators as conditions vary. The techniques also have an advantage as they complete the task of colour quantisation in a fraction of the time taken for an expert and so lend themselves well to the quick and efficient determination of automated sorting for a minerals application.

Future research should focus on the advancement and application of neural networks to colour quantisation in conjunction with tradition training methods Further to this research should concentrate on practical applications utilising a multi-sensor, multi-output approach to automated sorting.