Conclusions
The ability to create personalised mineral lists specific to each sample depending on their assemblage and chemical makeup, enables more accurate and in depth characterisation of samples, which is automatically quantified without the need for additional data manipulation after acquisition. It also allows the separation of chemically similar minerals, rather than grouping them together, consequently allowing for more accurate modal abundances to be created. This additional control in mineral identification can be used to inform decisions within the resource recovery process. However, when looking at samples that have a simple mineralogy with chemically distinct minerals, the automated mineralogy software may not be necessary, as standard techniques produce the same results.
The ability to set up individualised work flows, such as the Bright Phase Search used with the European gold mine sample, allows for tailored analysis, reducing the running time of data collection, as well as creating more detailed data of desirable minerals and grains to be collected from low grade ores, but this can also be applied to higher grade ore types. This additional data that is specific to the desirable minerals in the samples saves time as well as helping to inform any decisions made in the extraction process
Mineralogic overall is a powerful piece of software that allows for quick, in depth quantitative characterisation whilst requiring less human input. The large amounts of varied quantitative data that can be collected is an advantage as having more ‘Information reduces uncertainty about decisions which have economic consequences’ (Gu et al, 2014). In mining, reducing the uncertainties can allow for a more efficient and economic extraction process to be determined and put in place.