Dilution prediction in underground open stope mining using gene expression programming and backpropagation artificial neural network algorithms

Abstract

Unplanned dilution in underground mining is detrimental to the business, as imprecise dilution factors may impair production forecasts for existing operations or the economic evaluation and viability of brownfield expansions and greenfield projects. While high prediction accuracy of over 90% has been achieved using machine learning algorithms, particularly artificial neural networks (ANNs), the studies mostly predicted the overall dilution of stopes or included performance-subjective determinants, such as drill and blast factors. These factors compromise the models’ reproducibility for extensional application to cover new mining projects that do not have historical drill and blast input. To address this, the study explores gene expression programming (GEP) and ANN with backpropagation (BPNN) to predict dilution on a per-stope granularity based on geotechnical and design data. A 138-stope sample from a sublevel open stoping gold mine operation in Western Australia was used to generate predictive models. Model and infield results showed that the GEP model performed better, with a coefficient of determination, R2, of 0.740 with a root mean square error (RMSE) of 0.361 compared to BPNN's 0.681 and 0.409, respectively. Accordingly, the GEP model is recommended for dilution prediction for mine planning and production scheduling at the prescribed level of accuracy.