From Ore to Metal - Advanced Materials Characterisation by Automated Mineralogy

‘Automated Mineralogy’ terms an analytical method that is based on a combination of scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). We suggest to use the term ‘Automated Materials Characterisation’ instead in certain cases, since not only minerals but other solid substances can be characterised by this method. The range of solid natural and artificial materials that has been investigated includes minerals, ores, rocks, mineral beneficiation products and tailings, metals, alloys, slags, construction materials, ceramics, glasses as well as recycling and semiconductor materials and even extraterrestrial materials. Automated mineralogical investigations provide the unique opportunity to place quantitative constraints on a multitude of parameters tangible for the development and critical evaluation of beneficiation test work. This includes modal mineralogy, calculated elemental content, elemental distributions, mineral associations, particle and mineral size distributions, particle density distributions as well as liberation [3]. Sample overview images (‘mineral maps’) and images of specific mineral groups can also be extracted from the analysis data. Of particular value is Automated Mineralogy in the field of processing of mineral raw materials. Here, the analyses generate an improved material understanding and are used for evaluation and optimisation of mineral beneficiation processes. Application leads to the reduction of raw material losses coupled with an increased resource and energy efficiency, and thus higher revenue for the operation. Recent research has shown that automated mineralogy data can be used to establish particle-based grade-recovery curves for minerals and elements from feed compositions – and expand the assessment of process efficiencies to include particle distribution probabilities. The value of Automated Mineralogy is widely recognized in the minerals industry. Yet, its application is most widespread in the characterization of noble (Au, PGE) and base metal (especially Cu, but also Ni) ores and processing streams. The two case studies presented here for Li and Co ores serve to illustrate the potential of Automated Mineralogy during the beneficiation of minor commodities (such as Li) and beneficiation products, including by-products (such as Co). Automated Mineralogy is found to be an analytical method suitable to provide robust quantitative mineralogical data on very complex raw materials; current research extends its application to the characterization of recycling materials. The method is already very well established and an essential part of most studies in the field of processing of primary raw materials, whereas in the case of recycling raw materials only a few sample studies indicate the potential of the method.