The effect of geological uncertainty on achieving short-term targets: A quantitative approach using stochastic process simulation

Abstract

Continuous mining systems containing multiple excavators producing multiple products of raw materials are highly complex, exhibiting strong interdependency between constituents. Furthermore, random variables govern the system, which causes uncertainty in the supply of raw materials: uncertainty in knowledge about the reserve, the quantity demanded by the customers, and the breakdown of equipment. This paper presents a stochastic-based mine process simulator capturing different sources of uncertainties. It aims to quantify the effect of geological uncertainty and its impacts on the ability to deliver contractually defined quantities and qualities of coal, and on the system efficiency in terms of utilization of major equipment. Two different areas of research are combined: geostatistical simulation for capturing geological uncertainty, and stochastic process simulation to predict the performance and reliability of a large continuous mining system. The process of modelling and simulation in this specific production environment is discussed in detail. Problem specification and a new integrated simulation approach are presented. A case study in a large coal mine is used to demonstrate the impacts and evaluate the results in terms of reaching optimal production control decisions to increase average equipment utilization and control coal quality and quantity. The new approach is expected to lead to more robust decisions, improved efficiencies, and better coal quality management.