Mineralogic 101 – Complex Outputs

Mineralogic 101 – Complex Outputs

In the previous Mineralogic 101 post, we covered the general outputs available in Mineralogic through the Reporter software. However, there are further outputs that can be generated from the raw database either through SQL modeling or Microsoft Excel. Taken together these outputs improve the visualisation of complex data, enabling the client to obtain useful information on the data relevant to their mineral processing, environmental mineralogy or tailings control.

  • Grain Size Distribution
  • Further detailed liberation analysis, in particular
    • Theoretical grade-recovery curves
    • Liberation yield bar charts
    • Theoretical mineral-recovery curves
  • Mineral association data

These complex outputs are more important when the minerals textures are playing (or may play) an important role in limiting processing performance. Often (but not always), difficulties in processing boil down to liberation issues which is why there are several different tools available for visualising liberation characteristics within a sample.


Grain-size distribution (GSD)

This analysis returns the GSD of target phases and the overall particles. Although not a direct measure of liberation it is a very useful tool for visualising distributions that may explain poor recoveries. Often, in tailings, the target phase is fine-grained relative to the overall PSD (as in the example image where the chalcopyrite was finer-grained than the main gangue phases, with the strong suggestion therefore of high degrees of locking), but on occasion the target phase has displayed bi-modal distribution with a coarse-grained element that wasn’t being recovered due to insufficient residence time in the processing circuit.


Theoretical grade-recovery curves (TGR)

The TGR curve for an ore reports the maximum expected recovery of a mineral at a given grade.  As such TGRs are a great tool for visualising theoretical maximum recoveries and any departures from this. If there is a large difference between the achieved recovery and the theoretical maximum then there are opportunities for optimisation to the flotation process. In order to achieve a recovery greater than the theoretical maximum, it is necessary to increase liberation and therefore reduce the grind size. Observing how the TGR improves with reduced grind size is a powerful tool for visualising the necessary trade-off between the expense needed to grind finer and the improved liberation that results.


Liberation Yield Bar Charts

Liberation yield bar charts are a way of observing variations in liberation yield by degree of liberation across differing size fractions. As with the theoretical grade-recovery curve it can be a very useful way of seeing how liberation improves with increased grinding, and as in the example shown, there are usually significant improvements moving from the coarsest size fractions (+106 um) to the finest size fractions (+8 um). If enough discrete size fractions are analysed this information may also be used to consider the appropriate target grind size for liberation.


Theoretical mineral-recovery curves (TMR)

This is a powerful tool for visualising how the overall recovery of the target phase varies as different sized particles are recovered from the concentrate, feed or tailings. For highly locked target minerals a high proportion of the target phase will be recovered from locked coarser particles, before finally a few liberated minerals appear in the finest particle size classes (see figure). This analysis can be used to look at how liberation in the feed improves within finer particles, and therefore what an initial optimal grain size might be to achieve good liberation.


Mineral Association

Mineral association data is used to identify the main minerals associated with a target phase, and how these may vary in proportion and content across different size fractions. This can be particularly useful to determine the main locking minerals in prospective feeds or in tailings to improve the understanding of why minerals might not recover well during processing, or have failed to recover during processing. In the example shown there was a strong association of sphalerite, pyrite and chlorite with the non-floating chalcopyrite, which provided implications for potential adjustments to the flotation parameters.

Submit a Comment

Your email address will not be published. Required fields are marked *