Stochastic Predictions of Ore Production in an Underground Limestone Mine Using Different Probability Density Functions: A Comparative Study Using Big Data from ICT System

This study stochastically predicted ore production through discrete event simulation using four different probability density functions for truck travel times. An underground limestone mine was selected as the area. The time measured by analyzing big data acquired from information and communications technology (ICT) systems in October 2018, (uniform, triangular, normal, observed distribution of real data) were determined statistical values. A model a haulage system designed, times randomly generated Monte Carlo simulation. that fifty each function analyzed represented value lower 10% (P10), 50% (P50), 90% (P90). Ore underestimated when uniform triangular used, actual similar to P90. Conversely, P50 relatively consistent with normal data. root mean squared error (RMSE) predicting ten days 2018 lowest (24.9 ton/day) distribution.