Optimal sensor selection for sensor-based sorting based on automated mineralogy data

Assessing the success of sensor-based sorting in the raw materials industry currently requires time-consuming and expensive empirical test work. In this contribution we illustrate the prospects of successful sensor selection based on data acquired by scanning electron microscopy-based image analysis. Quantitative mineralogical and textural data from more than 100 thin sections were taken to capture mineralogical and textural variability of two different ore types from the Hammerlein Sn-In-Zn deposit, Germany. Parameters such as mineral grain sizes distribution, modal mineralogy, mineral area and mineral density distribution were used to simulate the prospects of sensor-based sorting using different sensors. The results illustrate that the abundance of rock-forming chlorite and/or density anomalies may well be used as proxies for the abundance of cassiterite, the main ore mineral. This suggests that sorting of the Hammerlein ore may well be achieved by either using a short-wavelength infrared detector - to quantify the abundance of chlorite - or a dual-energy X-ray transmission detector to determine the abundance of cassiterite. Empirical tests conducted using commercially available short-wave infrared and dual-energy X-ray transmission sensor systems are in excellent agreement with simulation-based predictions and confirm the potential of the novel approach introduced here. (C) 2019 The Authors. Published by Elsevier Ltd.