Unplanned dilution and ore loss prediction in longhole stoping mines via multiple regression and artificial neural network analyses

Abstract

Unplanned dilution and ore loss directly influence not only the productivity of underground stopes, but also the profitability of the entire mining process. Stope dilution is a result of complex interactions between a number of factors, and cannot be predicted prior to mining. In this study, unplanned dilution and ore loss prediction models were established using multiple linear and nonlinear regression analysis (MLRA and MNRA), as well as an artificial neural network (ANN) method based on 1067 datasets with ten causative factors from three underground longhole stoping mines in Western Australia. Models were established for individual mines, as well as a general model that includes all of the mine data-sets. The correlation coefficient (R) was used to evaluate the methods, and the values for MLRA, MNRA, and ANN compared with the general model were 0.419, 0.438, and 0.719, respectively. Considering that the current unplanned dilution and ore loss prediction for the mines investigated yielded an R of 0.088, the ANN model results are noteworthy. The proposed ANN model can be used directly as a practical tool to predict unplanned dilution and ore loss in mines, which will not only enhance productivity, but will also be beneficial for stope planning and design.