Comparative high pressure grinding rolls (HPGR) and cone crusher pilot-scale tests were conducted using Upper Group 2 (UG2) platinum-bearing ore in order to determine the impact of micro-cracks in HPGR products toward energy requirements in ball mills and the degree of liberation. The ball mill was fed with HPGR and cone crusher products of similar particle size distributions (PSDs). Qualitative analysis of the degree of micro-cracking on the HPGR and cone crusher products performed using scanning electron microscope (SEM) and image analysis software showed that an HPGR product had more micro-cracks than the equivalent cone crusher product. Milling energy requirements were evaluated using size-specific energy consumption indices calculated based on three grind sizes of 300 µm, 150 µm and 75 µm. The effect of residual micro-cracks in the products of HPGR and cone crusher on the milling size-specific energy requirement is inconclusive. The kinetic parameter k in the cumulative rate kinetic model for ball milling cone crusher products and for ball milling HPGR products were similar. Quantitative Evaluation of Minerals by Scanning Electron Microscopy (QEMSCAN) was used to determine the degree of liberation of various mineral phases in the mill products. At a coarser grind size (P₈₀ of 300 µm), the sulfides in the mill products pre-crushed using the cone crusher have consistently poorer liberation than in the equivalent HPGR pre-crushed sample. However, at a finer grind size (P₈₀ of 75 µm), the sulfides in the mill products pre-crushed using the cone crusher and using an HPGR showed similar liberation.

The study evaluated the milling kinetics of three copper ores, from a multi-mineralised deposit, which were identified as sulphide 1 (with bornite as a dominant copper mineral), sulphide 2 (mainly composed of chalcopyrite) and oxide (with malachite as a dominant copper mineral) and related the breakage parameters to the mineral composition data. Five mono-size fractions between 1000 µm and 212 µm were dry milled for short grinding times in the laboratory ball mill in order to obtain data for predicting breakage rate parameters. The analytical and mineralogical characterisation of the ores were performed using X-ray fluorescence (XRF) analysis, scanning electron microscopy energy dispersive spectroscopy (SEM-EDS) analysis, optical microscopy analysis and X-ray diffractometer (XRD). The mineralogy data showed that quartz was the abundant gangue mineral (average for each ore was above 60% (w/w)), followed by K-feldspar minerals (orthoclase and microcline) which constituted between 4% (w/w) and 6% (w/w) and the remainder are the minor calcite and dolomite minerals which are also in the host rock. The experimental milling kinetics parameters and mineralogical data were used to assess the robustness of the heterogeneous (two-component) and homogeneous (single-component) first-order rate breakage models. The mineral composition data were used for setting up the predictions of breakage parameters in the two-component and single-component first-order breakage models. The experimental data fitted better on the two-component breakage model than the single-component breakage model. These results highlighted the influence of two groups of minerals (generally classed as valuable and gangue minerals). The breakage data showed that the selection function for the hard component (the gangue minerals) has a dominant contribution to the overall selection function of the ores, with S_iA correlating fairly well with experimental S_i. The parameter a in the Austin empirical breakage model was relatively similar (approximately 1) for all three ores, which confirms similar milling conditions to which the ores were subjected to. The data suggests that there is a relationship between breakage parameter α (material-specific parameter) in the Austin empirical breakage model and brittleness index β_i (calculated from the mineralogical composition of the gangue phase). No clear trends could be deduced from the cumulative breakage distributions of the three ores. This highlights the complexity of developing relationships between the mineralogical composition data and breakage distributions of the ores which are extracted from the same deposit and with comparable gangue composition.