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Open Pit Mine Loading and Haulage Simulation Open Access


Other title
Mine hauling
Open pit mine discrete event simulation
Truck and Shovel discrete event simulation
Type of item
Degree grantor
University of Alberta
Author or creator
Montes Higuita,Luisa F
Supervisor and department
Hooman Askari (Civil and Environmental)
Examining committee member and department
Yashar Pourrahimian (Civil and Environmental)
Wei Victor Liu (Civil and Environmental)
Department of Civil and Environmental Engineering
Mining Engineering
Date accepted
Graduation date
2017-11:Fall 2017
Master of Science
Degree level
The objective of this research is to estimate the main KPI's of an open pit mining truck and shovel operations, while quantifying uncertainty about the KPI's with high statistical confidence. Short-term production scheduling base their estimations on deterministic approaches, but the nature of mining operation is variable, so when execution of the plan comes, it faces a reality different to what it forecasts. The main contribution of this thesis is to quantify uncertainty in truck and shovel KPI’s due to operational uncertainties, planned and un-planned maintenance, and weather events with a 95% confidence interval. To achieve the research objectives, a discrete event simulation model for truck and shovel operations is built, verified, and validated against historical dispatch data. The following tasks are completed: a) statistical data analysis of historical dispatch data, b) fitting probability density functions on historical operational data, c) building a truck and shovel simulation model in Arena software, d) adding the preventive maintenance, failures, and weather events into the simulation model for trucks and shovels, e) validation of equipment performances against historical data, f) four different major scenarios are assessed with changing the number of ore and waste trucks and the throughput rate of the crusher to find the near optimal size of the fleet, g) the detail short-term expected production of ore and waste under monthly and weekly time frames are reported along with KPI's for tonnage, time charts, availably, and efficiency. The main contribution of this research is a discrete event simulation model for truck and shovel operations that predicts the major KPIs of a mining operation with 95% statistical confidence about the statistics of interest while quantifying the uncertainty around the estimation.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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