Sensor-based particle-by-particle sorting is a technique in which singular particles are mechanically separated on certain physical and/or chemical properties after determining these properties with a sensor. Sensor-based sorting machines can be incorporated into mineral processing operations in order to remove waste or sub-economic ore prior to conventional treatment. This has potential to reduce the consumption of energy and water during mineral processing and thereby decrease processing costs. Furthermore, sensor-based sorting can be used to separate different ore types in order to enhance control of the feed to mineral processing facilities and improve processing efficiency. For most ore types no sensors are known that can be used to detect the grade of ore particles. This is because many ores are polyminerallic rocks in which the economically important minerals occur in relatively low concentrations and in small grain sizes. However, the deposition of ore minerals during the formation of hydrothermal ore deposits is often related to specific hydrothermal alteration zones. This means that it might be possible to characterise the grade of such an ore by using sensors that are capable of detecting differences in hydrothermal alteration mineralogy. Sensors can be applied throughout the entire mining value chain to collect information on the characteristics of the mined ore in real-time. The information that sensors provide can be used to improve deposit models, improve ore quality control and optimise mineral processing. However, the applicability of real-time sensor technologies has not yet been assessed for many types of ore deposits. The aim of the study was to explore the opportunities and potential benefits of using sensors for real-time raw material characterisation in mining and investigate the opportunities for sensor-based particle-by-particle sorting at hydrothermal ore deposits. Investigating sorting opportunities was aimed at researching the applicability of real-time sensors to segment waste particles from ore particles and to distinguish between ore particles that represent different ore types. This is based on samples taken from the Los Bronces porphyry copper-molybdenum deposit, the Lagunas Norte epithermal gold-silver deposit, and the Cortez Hills carlin-style gold deposit. For all the deposits included in the study, a fraction of the waste could be segmented by using a Visible to Near-InfraRed (VNIR) and Short-Wavelength InfraRed (SWIR) spectral sensor to detect the hydrothermal alteration mineralogy. For Lagunas Norte and Cortez Hills, this sensor could also be used to distinguish between different ore types. The ability to segment waste was based on indirect relationships between certain alteration mineral assemblages and the copper or gold grade. Since these relationships correspond to the alteration-mineralisation relationships that generally occur at each deposit type, there is potential that sensors can also be used to segment waste at other porphyry, epithermal or carlin-style deposits. For all three deposits additional research is required to investigate whether it is economically feasible to use the discrimination capabilities of the VNIR-SWIR spectral sensor for sensorbased particle-by-particle sorting. The feasibility may be limited by surface contaminations of the ore particles feeding the sorter, the influence of water on the discrimination capabilities of the VNIR-SWIR sensor, and the sorting efficiency resulting from misclassification. @en

Near-infrared (NIR) and short-wavelength infrared (SWIR) hyperspectral imagery can be used to detect certain alteration minerals. At epithermal deposits, the formation of alteration minerals is, in theory, related to the mineralisation of gold and silver. In order to provide foundations for developing sensor-based sorting applications at a mine that exploits such a deposit, it was investigated if NIR-SWIR hyperspectral imagery can be used to distinguish between ore and waste particles by characterising the alteration mineralogy. Maps were produced from the NIR-SWIR hyperspectral images of 827 drill core samples that show mineral occurrences, mineral absorption feature intensities and characteristics of the iron oxide mineralogy. Partial least squares discriminant analysis (PLS-DA) was applied to the information contained in these maps to investigate if this information can be used to discriminate between ore and waste. The results showed that NIR-SWIR hyperspectral imagery could be used to segment a population of waste samples by detecting occurrences of pyrophyllite, dickite and/or illite. This result can be explained by the fact that these minerals are commonly deposited further away from the ore-bearing epithermal veins, while the absence of SWIR-active minerals or detected occurrences of alunite are more closely associated with these structures. The ability to identify waste with NIR-SWIR spectral sensors means there is potential that sensor-based sorting can be used to remove this waste from mineral processing operations. Additional research is still required to assess the economic feasibility of such a sensor-based sorting application.@en

Sensors are being used as laboratory and in-situ techniques for characterization and definition of raw material properties. However, application of sensor technologies for underground mining resource extraction is very limited and highly dependent on the geological and operational environment. In our study the potential of RGB imaging, Fourier-Transform Infrared Spectroscopy (FTIR) spectroscopy and Hyperspectral imaging for the characterization of polymetallic sulphide minerals in a test case of the Reiche Zeche underground mine was investigated. @en

A recent study by Dalm et al. (2014) showed that alteration mineralogy acquired using SWIR point spectrometry could be linked to copper grade distribution for a group of samples from a South American copper mine. Since it was expected that SWIR hyperspectral imagery can provide more detailed information about the alteration mineralogy of these ores, we investigated whether this technique can be used to improve upon the indirect characterization of copper grades. Maps showing the distributions of SWIR-active minerals, white mica crystallinity, white mica composition, and chlorite composition were produced from SWIR hyperspectral images of 43 samples from the Dalm et al. (2014) study. Subsequently, a principle component analysis (PCA) was applied to the relative mineral abundances and the average white mica crystallinity and composition that were extracted from these maps. The PCA showed that this mineralogical data could be used to discriminate a significant portion of the samples with sub-economic copper grades. Furthermore, the study showed that SWIR hyperspectral imaging has the following advantages over SWIR point spectrometry: minerals that are present in relatively low quantities can be detected, the SWIR-active mineralogical composition at the surface of a sample can be quantified, and the texture of samples, such as grain sizes and cross-cutting vein structures, can be characterized. However, these advantages did not improve upon the indirect characterization of copper grades that was achieved using SWIR point spectrometry. This was attributed to the relatively small size of the sample set and the high textural variability between samples.@en