Shovel-truck cycle simulation methods in surface mining
Date
2008-04-16T08:10:17Z
Authors
Krause, Andre James
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
This study investigates the main factors of production, their interaction and influence
on cycle time efficiency for shovel-truck systems on surface mines. The main factors
are truck payload, cycle time and operator proficiency. It is now routine that shoveltruck
cycles are analysed using simulation methods. The Elbrond, FPC, Talpac, Arena
and Machine Repair simulation models are discussed to explain how their model
characteristics contribute to the differences in their reported cycle efficiency as
indicated by productivity results. The Machine Repair Model based on Markov chains
is adapted for shovel-truck systems and examined for calculating shovel-truck cycle
times.
The various probability distributions that can be use to model particular cycle time
variables and some methods in selecting the “best” fit are examined. Truck cycle time
variable sensitivity is examined by using the Excel® add-on program @Risk
(Palisade Corp.) in determining their respective weighting or contribution within the
total cycle time variability.
The analysis of cycle efficiency leads ultimately to sizing of a shovel-truck system.
When determining a fleet size for a particular surface operation the planning
engineers will tend to use one and to a lesser extent perhaps two separate simulation
models. This study calculates the productivity (tonnes per hour) for a “virtual mine”
with a variable number of trucks, variable cycle distances and variable truck loading
times. The study also includes a separate analysis of cycle time variables and their
probability distributions for the Orapa diamond mine in Botswana, to show possible
distributions for various cycle variables.
The study concludes with a calculation of the truck fleet size using the Elbrond, FPC,
Talpac and Arena and Machine Repair models for the Optimum Colliery coal mine
and then compares the results and their correlation.
The main findings are that the calculation of waiting time is different for the various
models, each model yields a unique fleet sizing solution and any solution in effect
represents a range of results.
Description
Keywords
probability distributions, queuing theory, bunching, maching, Monte Carlo simulation