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SEM-based automated image analysis is well established as a key tool in geometallurgical assessments, as it provides quantitative data on mineralogy and microstructure. It is also widely used in the mining industry to improve recoveries and to monitor process efficiency of processing plants. More recently, automated mineralogy has been also used to assess the presence and distribution of possible by-product or even penalty constituents. The approach at the Helmholtz Institute Freiberg of Resource Technology goes beyond these current applications: data from SEM-based automated analyses such as MLA in combination with complementary analytical methods (such as XRD and EPMA) is statistically assessed in order to predict the behavior of material during beneficiation. The purpose of this approach is to confidently reduce technical risk of raw materials projects whilst also reducing the need for empirical test work. This study will exemplify this approach with four very different case studies, including (1) on the recovery of Sn from a historic flotation tailings storage facility; (2) on by-product recovery from a chromite ore deposit, (3) on simulating sensor-based presorting; and (4) by-product recovery from a polymetallic base metal ore. All studies were performed by interdisciplinary teams including resource characterization, minerals processing and statistical modelling. 1) A predictive geometallurgical model of a tailings storage facility in the Erzgebirge (Germany) was created based the assessment and weighting of grade, modal mineralogy, liberation, grain size and flotation behavior of tailings intersected by a series of drill cores. All data was geo-referenced and combined to construct a 3D model illustrating the amount of cassiterite–bound tin that can realistically be recovered from the tailing. Results of this study illustrate the importance of combining different tangible parameters to assess the recoverable value that remains in industrial residues – such as flotation tailings. 2) A predictive geometallurgical model was created for an ore body comprising several stratiform chromitite seams in the Bushveld Complex (South Africa). The focus of this study was the assessment of the potential for PGE recovery as a by-product. Samples were collected from a series of drill core intersections of the different chromitite seams. More than 100 individual samples were studied in detail. Results were clustered, focusing on parameters relevant for beneficiation of PGE, such as PGE mineralogy, mineral association, grain sizes etc. These predictions were validated by selected metallurgical tests. Compositional clusters were then related back to well-known geological features. This integration of data served to define geometallurgical domains. 3) Assessing the success of sensor-based presorting currently requires time-consuming and expensive empirical test work. Yet, the prospects of success can be simulated with automated mineralogy data. This is illustrated using the example of a mineralogically and texturally complex skarn ore from the Hämmerlein Sn-In-Zn deposit, Germany. Cassiterite is the most important ore mineral and Sn is the major value constituent in the polymetallic skarn ore. The presence and abundance of cassiterite itself (< 4 Vol. %) is not a suitable target for sensor-based sorting. Yet, it appears intimately associated with a cogenetic chlorite-fluorite-sulfide assemblage. Parameters from MLA datasets, such as modal mineralogy 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 the density anomalies may well be used as proxies for the abundance of cassiterite. 4) The mineralogical deportment of Indium in mineralogically complex base metal sulphide ores from a mine in the Iberian pyrite belt was defined in order to constrain the potential to realize credits from this valuable by-product. Different to the previous case study, Indium does deport mostly into major ore-forming sulphides – and rarely forms its own ore minerals. The study is based on a combination of data from assays and MLA, data for geological and processing samples. In addition, an extensive set of mineral chemical data was acquired by EPMA to constrain the In deportment. Statistical regularities in the deportment of In are then used to predict In deportment from assay data alone. This predictive assessment includes statistical uncertainties, achievable recoveries and payable concentrate compositions. This, in turn, may be used in future mine planning. Key innovations introduced by these three case studies are of general applicability to other metals and ore types. They clearly illustrate the value of conducting predictive geometallurgical assessments already during the latter stages of exploration in a process that will benefit from regular follow-up during the phase of active exploitation.

This contribution introduces a 3D geometallurgical model that has been developed to assess the resource potential of PGE (+Ni and Cu) recovery at the Thaba chromite mine, South Africa. The Thaba Mine is located on the northwestern limb of the Bushveld Complex and is operated by Cronimet SA. The geometallurgical model is based on a structural model and geostatistical interpolation of primary geometallurgical properties, obtained from mineral liberation analysis (MLA) and geochemical exploration data. More than 250 MLA samples and 750 geochemical measurements cover four target seams (lower group chromitite seams: LG 6 and LG6a; middle group chromitite seams: MG1 and MG2). The data provides information about chemistry, modal mineralogy and micro-fabric from the platinum group minerals and associated base metal sulfides. The 3D model is based on the geometry and lithological interpretations of 300 drill cores and the known main faults in the study area and covers all stratigraphic units. The MLA measurements are linked to the 3D geometry of the target seams by the sampling positions in the associated boreholes. This allows to interpolate the information based on the MLA measurements over the horizontal extension of the target seams. The model can be enhanced with further information, e.g the distance to a volume of alterated silicate host rock. The final step is to transfer the geometallurgical model to a block model, that allows to predict for each block the parameters for an optimized processing. Our approach shows how the different ore types are spatially related to geological features and that a separate extraction of different geometallurgical ore types would be sensible.

This 3D geologic-stratigraphic modelling project is based on comprehensive drilling data originating from the Thaba mining lease area in the Western Bushveld Complex, South Africa. The geometric shape and distribution of manifold ultramafic and mafic rock types of the cyclic units of Lower and Upper Critical Zone of the Rustenburg Layered Suite is subject of this study. These 2.06 Ga old Paleoproterozoic successions occur as layered sequences at Thaba Mine and are characterized by shallow inclination towards SE (dipping angles 15° to 27°), distinct lateral and vertical continuity of the layering and the regular stratigraphic order of the horizons. Therewith, the requirements for the 3D geologic modelling approach are complied. The technique is called ‘Integrated Stratigraphic Modelling’ and is utilized by Maptek’s Vulcan modelling suite. The project is started by compiling a drilling database of more than 400 boreholes that host collar, survey, geology, and assay data. The present rock types of the source data are classified into five classes (chromitite, ‘silicate’, alteration, structure, burden) and a stratigraphic list of 42 horizons is developed based on the positions of chromitite horizons in the drilling data. Preparations for the modelling include modifications of original drilling data by applying the succession of horizons on the stratigraphy field in the geology table of the database. The developed stratigraphic horizon list is utilized by the software to sequence and interpolate floor and roof positions of each horizon. In total, the model consists of 21 chromitite layers (LG1 to MG4b) and 21 mafic interlayers. Interlayer horizons with the suffix -‘SIL’ comprise all rock types between two chromitite layers. Pyroxenite, harzburgite, and norite are most dominant; the higher in the stratigraphic column the more likely are rock types that contain cumulus plagioclase, e.g. anorthosites. Altered rock types such as troctolites and serpentinites are also occurring relatively frequent, but also discordant IRUP bodies as well as dykes and pegmatitic rocks. Chromitite layers are representing the basal parts of cyclic units with average thicknesses between a few centimeters and one meter, in some cases up to a few meters. Especially Lower Group chromitites are occurring with relative constant thicknesses over the entire study area. The farms Schilpadnest, south-west Zwartkop, and Elandskuil are parts of the study area where the layering is developed best. Middle Group chromitites and their mafic host rocks are distributed much more discontinuous on the farms Zwartkop (north-east), Roodedam, and Middellaagte. A pronounced lateral variation of the distribution of chromitites and host rock interlayers is recognizable in different parts of the study area, particularly areas of intense faulting between the farms Zwartkop and Middellaagte plus between Middellaagte and Elandskuil. Additionally, the consistency of the modelling results is reduced in areas were dominantly short boreholes occur, especially on the north-east section of farm Zwartkop.

Here we present information about concentrations, distribution and mineralogy of platinum group elements (PGE) in the sequence of chromitite layers from LG-6 to MG-4 at the Thaba mine in the north-western sector of the Bushveld Complex in South Africa. The information is based on assay analyses of drill core samples and studies of platinum group minerals and base-metal sulfides by mineral liberation analysis. Total concentrations of PGE gradually increase with the stratigraphic height from the LG-6 layer to MG-4. Detailed sampling of the LG-6, LG-6A, MG-1 and MG-2 layers revealed uneven vertical distribution of PGE with the highest concentrations at the footwall contacts. Pt-Pd ratios vary broadly from one layer to another and tend to increase from top to bottom within the individual layers. The assemblage of platinum group minerals in the LG-6 and LG-6A layers comprises equal proportions of (Pt,Pd)S, malanite and laurite, and minor amounts of Fe-Sn alloys sperrylite. PGE tellurides and bismuthotellurides are largely absent. In contrast, the PGE mineralogy of the MG-1 and MG-2 layers is dominated by laurite.

Geometallurgy distinguishes primary and secondary ore properties. Primary properties are describing the analytically observable properties of the ore, such as chemical composition, modal mineralogy or microstructure. Secondary properties describe the properties observable in processing experiments, like milling energy consumption, recovery and concentrate grade and thus finally monetary return as a function of processing decisions. While only the primary properties can be obtained in a sufficiently dense spatial grid at reasonable costs, the secondary properties determine processing decisions and the ultimate value. The standard approach is to estimate two things: A geostatistical block model of primary properties based on a spatial dataset and a regression model mapping the observable primary properties to experimental secondary properties on a smaller dataset. This regression model is then applied to the predicted primary properties. Due to the nonlinearity of the dependence and due to the difference in covariance structure between observed and predicted primary properties this is however misleading and will lead to suboptimal processing decisions and a loss in resource and energy efficiency. We solve this problem by defining intermediate properties, which can be computed directly from automated mineralogy data, on which processing properties depend approximately linearily. In this approach each particle observed is separated, remilled and circulated in virtual plants according to their observed microstructures. Nonlinear effects like the influence of the microstructure on the recovery are handled in this transformation. In this way most value relevant properties (recovery, dillution and mass pull, etc.) become a linear function of the virtual distribution of microstructures. This multivariate dataset of intermediate values is then predicted by standard geostatistical techniques. The methodology will be exemplified with a 2D case study from a chromite mine based on local geological knowledge and automated mineralogy data acquired on a suite of samples from known locations. The resultant model readily illustrates domains of differing processing characteristics

The mineralization of economically important platinum group elements (PGE) mainly occurs in mafic-ultramafic intrusions such as the Bushveld Complex. PGE are currently mined from the Merensky Reef, the Platreef and the UG-2 chromitite, which is the only chromitite layer mined for PGE, however, the Lower Group (LG) and Middle Group (MG) chromitites also contain significant PGE concentrations ranging from 0.5 to 9 g/t. In the pristine ores, PGE occur both as discrete platinum-group minerals (PGM) and included within sulfides. Previous studies showed that in the LG-6 and MG-1/2, PGE-sulfides (cooperite-braggite, malanite, laurite) are the dominant PGM, followed by PGE-sulfarsenides, sperrylite and Pt-Fe alloys. The rocks in the Bushveld Complex are strongly weathered down to 10 to 40 m below the surface. Near-surface chromitite seams of the LG and MG of the western Bushveld Complex at the Thaba Mine are studied by electron microprobe and LA-ICP-MS for the distribution of PGE. The analytical conditions employed for silicates were: 20 kV acceleration voltage, 120 nA beam current, focused beam, and up to 360 s measuring time. LA-ICP-MS measurements were carried out on a ThermoScientific Element XR (HR-ICP-MS) coupled with a femtosecond laser ablation system. During weathering, sulfides and PGM are largely destroyed and PGE are mobilized and redistributed in the near-surface ores. Within the near-surface weathered chromitite seams, only relict PGM can be observed, which are observed within silicates or as inclusions in chromite. Former grains of PGM are observed in secondary silicates showing larger shapes but are locally redistributed along small cracks and Pt-Fe-(oxides) are observed as well, arguing for the neoformation of PGM. Secondary silicate minerals in weathered ores show concentrations of Pt and Pd in the 100s-ppm range. A good correlation can be observed with Ni and Pd, arguing for the weathering of pentlandite, which hosts Pd in the pristine PGE ore. Palladium and Ni partly remained and were incorporated into secondary silicates while S is largely lost.

The identification and accurate characterization of discrete Indium minerals is usually a very cumbersome procedure due to their small grain size (typically below 10 μm) and complex mineral assemblage in massive sulfide mineralizations. A novel strategy for finding discrete In minerals and quantifying their composition was developed by using a mineral liberation analyzer (MLA) and an electron microprobe analysis (EPMA). The method was successfully applied to polymetallic massive sulfide ores with an incompletely known mineralogical composition from the Neves-Corvo deposit in Portugal. The occurrence of roquesite and sakuraiite could be systematically detected, their concentration quantified by MLA measurements, and their identity later confirmed by EPMA analyses. Based on the results obtained, an almost complete deportment of In was obtained for the six samples studied. This supports the approach taken, combining automated mineralogical data with electron microprobe analysis. A similar approach can be easily applied to other common minor and trace elements in complex base-metal sulfide ores, e.g. Se, Ge, Sb, or Ag, thus permitting targeted development of resource technologies suitable for by-product recovery.

The chromitite seams of the Lower Group (LG) and Middle Group (MG) of the Bushveld Complex in South Africa contain elevated Pt and Pd concentrations. So far, only limited work has been done on the distribution of platinum-group elements (PGE) and platinum-group minerals (PGM) in these ores. In particular, the knowledge about the effects of weathering of PGM and the redistribution of PGE in these ores are rare. Previous studies showed that the main PGM are PGE-sulfides (cooperite-braggite, malanite, laurite), followed by PGE-sulfarsenides, sperrylite and Pt-Fe alloys. During weathering PGM are largely destroyed and only relict PGM are observed. Platinum-group minerals are weathered along small cracks and neo-formation of PGM may take place. Additionally, the mineralogical siting of Ru within chromitites is studied here. It was shown by LA-ICP-MS work that chromite can host some 100s-ppb of Ru.