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The standard JK drop weight test breaks single particles at five size fractions each at different energy levels by dropping a given weight from a certain height. The progenies of all particles at a given energy level are sieved and the degree of breakage of all those particles at that energy level is presented as t10% (percent passing 1/10th of original size). This method assumes that all of the particles in a single size fraction have the same mass and experience a similar degree of breakage and calculates the average breakage characteristics of an ore domain from impact loading. Hence, this standard approach does not capture the breakage variability that exists among ore particles. To investigate the breakage variability of ore, the drop weight testing method has been extended where the energy applied and the degree of breakage for each particle within the sample are measured separately. Therefore, the degree of breakage for the ore domain is expressed as an envelope of t10% vs energy curves. This approach provides some insight into the intrinsic variability of the response to impact loading within an ore domain. Several rock types were tested by the extended DWT testing approach and the implications of ore breakage variability for a SAG mill treating these materials are simulated for a standard SAB circuit using the JKMRC grinding models.

The heterogeneous nature of orebodies introduces large uncertainties into all quantitative evaluations, process design and process predictions. Measuring the extent of the variability of ore competence will allow the design process to account for variation in process performance through a quantitative knowledge of its uncertainties related to ore hardness. The conventional JKMRC drop-weight test (JKDWT) establishes the relationship between input energy (Ecs) and product fineness (t10) from which the breakage potential parameter A × b can be estimated, by combining the broken progeny of groups of particles. A new method, the Extended Drop Weight Test (ExDWT), has been developed which is applied to individual particles and is therefore capable of capturing breakage heterogeneity at high resolution. This paper explores a number of features of the new method, based on breakage tests on individual particles from several different rock types. The results showed that more accurate descriptions of particle size resulted in higher (softer) A × b values which suggests that the standard method may have been over-estimating rock competence. Regular-shaped cores broken diametrally were found to have higher (softer) A × b values than axially broken cores and irregular shaped particles. These tests also suggested that the true ore intrinsic heterogeneity is the main source of breakage variability measured by the ExDWT. The mean A × b values determined by the ExDWT showed no statistical difference to those determined by the standard JKDWT method, but the standard deviation of the estimate was much lower. The results have demonstrated the potential of the new method for capturing the inherent heterogeneity of individual ore particles. Such information could be used to populate multi-component models of comminution.

This paper examines the hypothesis that the manual selection of rocks for inspection, testing or analysis is invariably biased towards the heavier (larger) particles in the population being sampled. If the property of interest, such as assay or breakage potential, is size-related then such a bias would lead to systematic errors in the estimation of this property. To test the hypothesis, human volunteers were asked to select a sample of 10 rocks from a tray of 100 rocks of known weights, with and without a blindfold, in duplicate. This was repeated for a number of different rock size ranges in the range −50 + 19 mm. A statistical analysis of the results confirms the hypothesis that in almost all cases the samples were of larger weight than that expected from the known weight of the population of rocks. The magnitude of the bias depended on conditions but was highest for the widest size range. It is also shown that the volunteers produced different results to each other. The blindfold reduced the bias in the narrow size ranges but increased it for the wide size range. These effects are likely to be less important for populations of narrow size range, but where a truly unbiased sample is required strategies are proposed using randomisation processes. Relying on unmoderated human selection will lead to samples which overestimate the weight of the population.