483 operator skill and training. The implication is that even if the same operator was to undertake repeat measurements on a given sample, the resulting dataset will comprise a distribution of values. All measurements have error, which can never be eliminated, and therefore must be accounted for when undertaking rigorous data analysis. In mineral processing plants, these unfortunate truths conspire to camouflage the true effect of experiments that operators and production metallurgists must undertake to identify strategies that improve processing performance. Consider a copper concentrator processing ore of 1% Cu grade at 1000 t/h. A recovery improvement of 2% would be worth $11 M USD per year, assuming 8000 operating hours per year and a conservative copper price of $7000 USD per tonne. The problem is: how can this small change be detected in production data where recovery could vary by 10-20% (Figure 1)? Figure 3 – Actual metal recovery data from a typical concentrator, ranging in this example from 82-94% recovery. The answer lies in the application of mature, statistical concepts to design well-structured experiments that block out the effects of nuisance, uncontrollable variables and random error. The effect of the trial can then be identified with the use of statistical analysis that separates it from ‘noise’ in the data. The result is that, if an effect is real, it can still be observable, even in the midst of overwhelming variability in the data. Conversely, if the trial has had no effect, this will also be apparent. In either scenario, wellinformed business decisions can then be made, where the risk of poor decision-making can be effectively quantified and managed. This becomes critical when investment is needed to facilitate the change being investigated, for example, if higher operating expenditures or even significant capital expenditures will be incurred. Three case studies are presented in the following sections to demonstrate the power of this approach in facilitating continuous improvement at a production-scale. 2. QUANTIFYING METAL RECOVERY IMPROVEMENTS WITH A RANDOMISED PAIRED TRIAL The randomised paired trial is perhaps one of the most powerful experimental designs in minerals processing, due to its ability to detect small differences that exist between comparative datasets otherwise subject to large degrees of uncontrolled variability. The experimental design is established so that ultimately, comparisons are undertaken within data pairs that are most ‘similar’ (i.e. as similar as is possible to achieve 80 82 84 86 88 90 92 94 96 0 10 20 30 40 50 60 Recovery (%) Day
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