Nowadays, new deposits are rarely developed for ore extraction, which is why partially depleted mines continue to operate. Due to the decrease in the concentration of valuable material, e.g. copper ores in most mines, the mass of material to be processed increases in order to obtain the same amount of the end product. This also increases the amount of water and energy used to extract these raw materials.
One way to counteract this depletion is to pre-concentrate the material stream by sensor-based sorting. In this process, tailings, the processing of which contributes only a minimal share to the end product, are removed early in the process chain. Since the crushing and grinding of the rocks in the mining operation requires large amounts of energy, there is great potential for savings here. Pre-sorting the material stream thus enables a reduction in the resources expended per ton of end product, such as energy and water, as well as consumables and reagents in the later process steps.
The basis of such sorting is a sensor system that makes a prediction of the concentration of the desired mineral or element for each individual particle. Of the possible sensor technologies for sorting, ME-XRT (multi-energy X-ray transmission imaging) and LIBS (laser-induced breakdown spectroscopy) were selected because they can provide complementary compositional information. ME-XRT is a transmission technique and provides information on the total irradiated volume of the sample, but is limited in the accuracy of elemental information, as it only provides information correlated with the effective atomic number. LIBS, on the other hand, is limited in analysis to a comparatively small area of the surface, but can provide more accurate information about the composition of chemical elements. By fusing the data from both sensor systems, the gain in accuracy of the prediction of the concentration that can be achieved as a result should be demonstrated. The accuracy of the prediction of the concentration should be studied using conventional methods for each sensor technology separately as a reference value. Using machine learning (ML) methods, specifically deep neural networks, the data from the sensor technologies should be examined both individually and in combination (data fusion).
The accuracy of the prediction of the concentration of the desired mineral or element should then be used to derive the possible savings in water and energy with appropriate presorting. Furthermore, the usability of the sensor information in the overall geological picture should be investigated. This should be demonstrated using Chilean copper and iron ores as an example.