CIM Vancouver 2010
Juliana Parreira, John Meech,
Abstract During the past decade there have been record increases in global demand for minerals and metals. At the same time, there has been a significant decline in skilled personnel. Mining companies are beginning to examine new technologies to maximize production, reduce costs, and create safer working conditions to deal with this shortfall. Automation of an open-pit mine haulage system is receiving attention as a beneficial option since it provides more consistent and efficient operation of mining equipment, it removes workers from potential danger, it reduces fuel consumption significantly reducing greenhouse gas (GHG) emissions, and it can help optimize vehicle repairs and equipment replacement by having more-predictable and better-controlled maintenance. This paper describes the development of a discrete event model built using EXTENDSIM software to examine scale-up issues and constraints of autonomous vehicles in comparison to a manual system. The software allows for the prediction and management of Key Performance Indicators (KPI’s), such as productivity, safety, cost, equipment failures, fuel consumption, and tire wear under different road and load conditions. Breaking-down the various systems of an autonomous haulage truck and simulating them using a Monte Carlo approach enables the model to become a flexible and powerful tool that can be incorporated to organizational process. A direct comparison to a manually-run system can be made to show the cost and operational benefits of automation. Project managers can use this tool to guide decision making about the possible application of autonomous haulage trucks in a specific mine.
Keywords: KPIs, modeling, Simulation, autonomous haulage trucks, Business process management
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