Determination of the optimal sensor for ore sorting, M. Kern, L. Tusa, M. Khodadazadeh, T. Leißner, R. Gloaguen, K.G. van den Boogaart, and J. Gutzmer

Ore sorting is a technology that is increasingly used to process primary raw materials. Timeconsuming and expensive empirical state-of-the-art test work is carried out to assess whether the use of ore sorting for the enrichment of a particular ore makes technical and economic sense. With the innovative simulation-based approach presented here, it is possible to direct the selection of a suitable sensor based on quantitative mineralogical and textural data, thus avoiding much of the empirical studies. Required data can be collected quickly and cost-effectively using available methods of automated mineralogy. The obtained parameters such as mineral grain size distribution, modal mineralogy, mineral area and mineral density distribution have been utilised in this study to simulate the prospects of success of ore sorting, applying different types of sensors. Empirical tests with commercially available sensor systems have been conducted to experimentally validate the predictions of the simulations. The estimation of the target mineral grade can be further optimised by the use of machine learning algorithms for the integration of automated mineralogy data and sensor data. The approach can easily be adapted to other types of raw materials and thus has great potential to become a key technology for the optimisation of processing experiments. Keywords: Automated mineralogy, hyperspectral imaging, machine learning, ore sorting