Predicting the overall equipment efficiency of core drill rigs in mining using ANN and improving it using MCDM

Abstract

In this manuscript, an attempt has been made to predict and improve the overall equipment effectiveness of core drill rigs. A combined Box– Jenkins and artificial neural network model was used to develop a three parameter model (drill pushing pressure, drill penetration rate & average pillar drill pit cycle time) for predicting effectiveness. the overall equipment efficiency of core drill rigs. The values of mean average percentage error, root mean square error, normalized root mean square error, men bias error, normalized mean biased error and coefficient of determination values were found to be 9.462%, 17.378%, 0.194, 0.96%, 0.0014 and 0.923. Empirical relationships were developed between the input and output parameters and its effectiveness were evaluated using analysis of variance. For attaining 74.9% effectiveness, the optimized values of pushing pressure, penetration rate and average pillar drill pit cycle time were predicted to be 101.7 bar, 0.94 m/min and 272 min, which was validated. Interactions, perturbations and sensitivity analysis were conducted.