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

Abstract

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

Simulation of production processes and associated costs in mining using the Monte Carlo method

“…For example, Brzychczy (2018) makes use of stochastic networks combined with Monte Carlo analysis to simulate and optimize the performance of longwall operations in coal mining. Jung, Baek, and Choi (2021) propose a discrete event simulation of production in an underground limestone mine. Upadhyay and Askari-Nasab (2018) likewise suggest a discrete event simulation for a shovel-truck production system in opencast mining.…”

Diagnosis of Problems in Truck Ore Transport Operations in Underground Mines Using Various Machine Learning Models and Data Collected by Internet of Things Systems

“…Because the productivity and profits of mines can vary greatly depending on the design and planning of the production process, optimal operation methods and equipment utilization strategies are needed to maximize productivity and equipment efficiency and minimize operating costs [1][2][3][4][5]. The cost of transporting ore and waste accounts for over 50% of the total mine operational cost, therefore, it is crucial to design and operate the transport system efficiently [6].…”