Publikationsrepositorium - Helmholtz-Zentrum Dresden-Rossendorf

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.