Block models provide a spatial representation of the known information of an orebody from which investment, development and operational decisions can be made. From a planning context, a single blockA spatially constrained unit within the resource model. within a block model provides the minimum resolution of interpolated information that can be used to inform operational decisions.
Planning decisions rely on block models that are unbiased and globally correct to accurately forecast production. They therefore favour estimation techniques that reduce variability within the sample set to provide robust estimates for large tonnages. As data density increases, from infill and ore control drilling, the ability to reduce blockA spatially constrained unit within the resource model. size captures increasing heterogeneity within short-term block models which improves eventual ore control delineations. Generally, there is no need to increase resource modelDigitised model of the deposit. resolution beyond short-term models as material allocation decisions are limited by minimum mining volumes.
Grade Engineering can radically alter minimum separation volumes providing even greater resolution for material allocation decisions. As such, there is a need to estimate heterogeneity at significantly smaller volumes then typically represented in resource block models to approximate Grade Engineering performance for differential blasting and bulk sorting techniques.
Increasing block model resolution can be achieved by:
· increasing data density to support interpolation of smaller blockA spatially constrained unit within the resource model. sizes within a resource modelDigitised model of the deposit., or
· including probabilistic estimates of grade and geometallurgical properties of a blockA spatially constrained unit within the resource model. based on underlying sample variability (e.g. Uniform Conditioned resource modelDigitised model of the deposit.s), or
· simulation of variability within smaller blockA spatially constrained unit within the resource model. sizes based on variability within sampled data set (e.g. conditional simulation resource modelDigitised model of the deposit.s).
Greater detail regarding Uniform Conditioning and conditional simulation can be found here.
Using high-resolution block models to estimate differential blasting and bulk sorting performance requires assessment of the tonnages, grades, spatial location and geometallurgical properties of material above a given cut-off. A relatively simple comparison of the high-resolution resource modelDigitised model of the deposit. to the standard resource modelDigitised model of the deposit. provides an indicative value proposition for Grade Engineering techniques that reduce minimum separation volumes. Figure 1 provides an example of additional selectivity achieved by reducing selective mining unit size from 30kt to 300t, resulting in increased metal content and reduced tonnages to processing activities.
However, several caveats apply to curves in Figure 1. Firstly, changes should always be assessed from the current selective mining unit. Secondly, the curves assume all material (ore and waste) will pass through the process that enhances selectivity. Thirdly, the curves are based on in-situ measurements and modelling and do not consider the homogenisation of material prior to processing.
Figure 1: Example of enhanced selectivity through reduction in selective mining units
Uniform conditioned probabilities usage to estimate differential blasting and bulk sorting performance is presented in the Neytiri example. Processes also apply to the use of conditional simulated block models and higher resolution block models.
For planning and production decisions, the information contained within higher resolution block models should be represented within the standard blockA spatially constrained unit within the resource model. size. This is because the standard blockA spatially constrained unit within the resource model. size will continue to define the minable shapes of the orebody and forms the basis for pit and phase designs. The higher-resolution data, informing enhanced selectivity opportunities, should be evaluated when calculating the value of the standard blockA spatially constrained unit within the resource model. if allocated to different processing pathways. Using this approach the optimal allocation of the orebody, with and without Grade Engineering, can be compared.
In summary, Grade Engineering techniques that improve the selectivity of ore and wasteMaterial determined to be below a predetermined grade or economic threshold. require the use of higher resolution data than typically interpolated within the resource modelDigitised model of the deposit.. There are several techniques that can be used to include heterogeneity at separation scales of interest, but there are limitations associated with these techniques. While these techniques can be used to evaluate Grade Engineering potential for bulk sorting and differential blasting, ultimately additional data at separation scales of the GEGrade Engineering technique is required to confirm the optimal use of Grade Engineering in the development of the orebody.