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The ball mill is usually the largest energy consumer at a mine site and significantly affects operational expenditures. Given a target particle size, Bond Mill Work Index estimates are used to predict a ball mill's throughput. In order to maximize ball mill throughput and optimize energy utilization, it is important to get these estimates right. At the Tropicana Gold Mine, Work Index estimates, derived from X-Ray Fluorescence and Hyperspectral scanning of Grade Control samples, are used to construct spatial GeoMetallurgical models (GeoMet). Inaccuracies in block estimates exist due to limited calibration between grade control derived and laboratory Work Index values. To improve the calibration, an updating algorithm has been tested at the Tropicana Gold Mine. The aim of the study was to demonstrate a new process for updating block estimates using actual mill performance data. Deviations between predicted and actual mill performance are monitored and used to locally improve the Work Index estimates in the GeoMet model. The updating algorithm improves the spatial Work Index estimates, resulting in a real-time reconciliation of already extracted blocks and a recalibration of future scheduled blocks. The case study shows that historic and future production estimates improve on average by about 72 and 26%.

Characterization of spatial variability in earth science commonly requires random fields which are stationary within delineated domains. This contribution presents an alternative approach for simulating attributes in combination with a non-stationary first-order moment. A new procedure is presented to unambiguously decompose the observed behaviour into a deterministic trend and a stochastic residual, while explicitly controlling the modelled uncertainty. The practicality of the approach resides in a straightforward and objective inference of the variogram model and neighborhood parameters. This method does not require a prior removal of the trend. The inference principle is based on minimizing the deviation between empirical and theoretical errors calculated for increasingly distant neighborhood shells. Further, the inference is integrated into a systematic simulation framework and accompanying validation guidelines are formulated. The effort results in a characterization of the resource uncertainty of an existing heavy mineral sand deposit.

The mining industry continuously struggles to keep produced tonnages and grades aligned with targets derived from model-based expectations. Deviations often result from the inability to characterise short-term production units accurately based on sparsely distributed exploration data. During operation, the characterisation of short-term production units can be significantly improved when deviations are monitored and integrated back into the underlying grade control model. A previous contribution introduced a novel simulation-based geostatistical approach to repeatedly update the grade control model based on online data from a production monitoring system. The added value of the presented algorithm results from its ability to handle inaccurate observations made on blended material streams originating from two or more extraction points. This contribution further extends previous work studying the relation between system control parameters and algorithm performance. A total of 125 experiments are conducted to quantify the effects of variations in measurement volume, blending ratio and sensor precision. Based on the outcome of the experiments, recommendations are formulated for optimal operation of the monitoring system, guaranteeing the best possible algorithm performance.

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