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

Dahee Jung,Jieun Baek,Yosoon Choi

doi:10.3390/app11094301

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.

Highlights

A mine project generally seeks to maximize production profits by using minimum capital and operating costs over the mine’s lifetime [1]
The results indicate that the case using observed probability distribution based on the big data of an information and communications technology (ICT) system showed the best performance for ore prediction
The truck cycle time (TCT) was measured by dividing it based on the four loading points, using the truck tag recognition time record big data obtained through the mine safety management system based on a wireless communication network for an underground limestone mine

Summary

Introduction

A mine project generally seeks to maximize production profits by using minimum capital and operating costs over the mine’s lifetime [1]. Mines require optimal operating methods and equipment utilization strategies to increase productivity and minimize operating costs. Efforts are being made to efficiently operate a truck haulage system, which constitutes greater than half of the operating cost [2]. The efficiency of a truck haulage system varies depending on the combination of the equipment used and operating patterns [3]. It is necessary to operate an optimal truck haulage system capable of maximizing ore production and minimizing the equipment delay time [4]. An effective method for operating truck haulage systems is to simulate a virtual system model, using various optimization techniques. For the virtual system model, a simulation algorithm can be designed based on discrete events comprising truck haulage operations, such as traveling, spotting, loading, dumping, and queuing. Several algorithms for truck haulage systems have been developed based on linear programming [5,6,7,8], genetic algorithms [9], queuing theory [10,11,12,13,14,15], fuzzy logic [16], and deep neural networks [17,18]

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Methods

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Conclusion