Abstract
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.
Highlights
The mining industry has had mixed successes in achieving its production targets (Ward and McCarthy 1999; Vallee 2000; Tatman 2001; McCarthy 2003)
The 144 updates in experiment 111 reduce the root mean square error (RMSE) in bench A and B with 0.464 (64.56%) and 0.475 (60.78%)
A new algorithm has been developed to update repeatedly the grade control model based on online data from a production monitoring system
Summary
The mining industry has had mixed successes in achieving its production targets (Ward and McCarthy 1999; Vallee 2000; Tatman 2001; McCarthy 2003). The deviation of produced tonnages and grades from model-based expectations often results from a mismatch in scale between exploration data and short-term production targets (Benndorf 2013). Sparse data from holes drilled at relatively wide grids is by no means sufficient to characterise truck loads accurately, designated to be fed into the processing plant. Grade control (GC) drilling can reduce the uncertainty to some extent (Peattie and Dimitrakopoulos 2013; Dimitrakopoulos and Godoy 2014). The uncertainty can be further reduced by assimilating abundantly available sensor measurements into the GC model (Wambeke et al 2018). Sensor measurements are significantly more noisy and do often characterise a blend of material originating from multiple extraction points. Wambeke and Benndorf (2017) developed an algorithm capable of handling these specific challenges
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