The effect of mineralogy on the grindability was investigated using three copper ores - two sulphides and one oxide. The dominant copper minerals were identified by optical microscopy and mineral chemistry derived from SEM-EDS analysis. The sample designated sulphide 1 was bornite-rich, sulphide 2 ore was mainly chalcopyrite, and the oxide ore was predominantly malachite and minor azurite. The gangue minerals were identified using semi-qualitative XRD analysis. Sulphide 1 contained more than 80% (w/w) of quartz compared to about 70% in the other two ores. The Bond work indices were 13.8, 21.6, and 17.3 kWh/t for sulphide 1, sulphide 2, and oxide ore respectively. This suggested that the chalcopyrite-rich ore is the hardest, while the malachite-rich ore has intermediate hardness, and the bornite-rich ore is the softest. The brittleness indices of the ores were calculated using the chemical composition of the gangue, and a good correlation between brittleness indices and Bond work indices was observed, which highlights the importance of the gangue composition in determining the fracture behaviour of the ores. There is scope for further investigation into the relationship between ore mineralogy and comminution behaviour using other breakage characterization techniques.

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 SiA correlating fairly well with experimental Si. 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 βi (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.

SYNOPSIS Electrowinning consumes a substantial amount of electrical energy, and owing to the ever-increasing unit cost of electrical power there is a need to improve current efficiency in the process. This research was carried out to design a continuous quality improvement framework for improving electrowinning current efficiency by applying statistical process control (SPC) on an online industrial copper electrowinning operation. A sequential mixed research methodology was applied and a statistical software package utilized for analysing data. It was concluded that metallurgical short-circuits (hotspots) had the most significant effect on current efficiency. Bringing hotspots under statistical control resulted in a 5.40% improvement in current efficiency, which is equivalent to approximately 74 t of 99.999% pure grade A copper cathode production over a period of 1.5 months. Keywords: quality, continuous improvement, continuous quality improvement, statistical process control, and current efficiency.

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