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Study of truck & shovel operations productivity using simulation platform MineDES
Study of truck & shovel operations productivity using simulation platform MineDES
Dmitry Kostyuk
Dmitry Kostyuk
Specialist Scientist, Group Resource and
Specialist Scientist, Group Resource and Business Optimisation
Business Optimisation
25 November 2014
Contents
Contents
S
Simu
imulation
lation M
Modeling
odeling in
in M
Mine P
ine Pla
lanni
nning
ng
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T
ruc
ruc
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sho
sho
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opera
opera
tio
tio
ns
ns
sim
sim
ulation
ulation
platform
platform
M
M
ineD
ineD
E
E
S
S
•
•
Overview
Overview
•
•
A
A
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d
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v
an
an
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t
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ag
es
es
•
•
Be
Be
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Conclusions
Mine Planning Challenges
• Mines will continue to increase in depth, scale and complexity.
• There is a strong demand for mine planners to:
–Produce achievable, optimized plans for these mines
–Appropriately size equipment fleets
–Design efficient, effective mine access systems
–Accurately estimate mining system productivity
• Getting it wrong can negatively impact project NPV.
Conventional Mine Planning
• Analytical methods – formula-based approach
• High-level of abstraction – BIG PICTURE point of view
• Normally doesn’t account for the impact on overall mining system productivity
such factors as equipment interactions, parameters variability, randomness,
uncertainty etc…
• Fails to describe systems with dynamic behavior featuring:
– non-linear behavior
– non-intuitive influences between variables
– time and causal dependencies
Mine Planning using Simulation Modeling
Simulation Modeling
• Method of solving problems that
can’t be calculated analytically
• Cheap and risk free experiments
(“what if ?” studies)
• Efficient for analyzing systems with
dynamic behavior
Analytical Modeling vs. Simulation Modeling
Ar ri val s: on aver age
trucks / hour
Loading time exponentially distr ibuted:
1/ – mean loading time
Shovel utilization:
Average waiting time:
Average queue length:
Loading time arbitr ary distribut ed:
1/ – mean loading time
Shovel utilization:
Average waiting time:
,
where
is coefficient of variation of
loading time.
Single lo ader
Poisson stream (independent arrivals)
Case #1:
Case #2:
Analytical Modeling vs. Simulation Modeling
Ar ri val s: on aver age
trucks / hour
Multiple (K) loaders
Loading time exponentially dist ributed:
1/ – mean loading time
Shovel utilization:
Average waiting time:
,
where P
!
,
and
!
∑
!
Loading time arbitr ary distribut ed:
1/ – mean loading time
Poisson stream (independent arrivals)
ANALYTICAL SOLUTION
DOES NOT EXIST STARTING
FROM HERE AND FOR ANY
FURTHER COMPLICATION
OF THE PROCESS!!!
Case #3:
Case #4:
Advantages of Simulation Modeling
• Enabling system analysis, and to find solutions where other methods fail
• Once appropriate level of abstraction is selected, development of a simulation model is a more
straightforward process than analytical modeling – less intellectual efforts, scalable, incremental
and modular
• The structure of a simulation model naturally reflects the structure of the real system – it is
visual, easy to verify and communicate to other people
• Any state of the model is measurable and any entity, which is not below abstraction level is
tractable – sensitivity analysis, statistical analysis
• Ability to play and animate the system
Contents
Simulation Modeling in Mine Planning
Truck and shovel operations simulation platform MineDES
•
Overview
•
Advantages
•
Benchmarking and validation
Case Study
MineDES - What is it?
• Truck & shovel operations simulation tool that can be used to estimate mining movement and
processing capability in the following dimensions:
– productivity statistics
– bottleneck processes and infrastructure
– truck queuing statistics
– the impact of different crew and maintenance schedules
– the impact of unscheduled random equipment and road sector downtime
– the influence of road maintenance vehicles and light vehicles on congestion
– the capacity of particular pit ramps, and the whole road network, to support planned material
movements.
MineDES Features
• Truck dynamics are calculated using full rim-pull curve data,
taking into account truck payload as well as road gradient and
quality
• A fast, purpose-built simulation engine, and the modelling of
trucks as agents to ensure realistic and intelligent behavior
• Truck dispatch is predicated upon attempting to achieve a
user-defined mining rate at each mining face
• Realistic and flexible modelling of traffic rules at complex
intersections
• Modelling of payload and loading/dumping time variability
• Flexibility in assigning legal digging and dumping destinations to different truck sets within each truck fleet
• The optional application of a wide range of scheduled and unscheduled downtime for all mobile and static
Advantages of truck & shovel simulator MineDES
• In-house software development project
with more than 4 years development
history.
• Developed from scratch and doesn’t use
any commercial simulation engines.
• Designed to address both long- and
short-term mine planning problems
• Intuitive, flexible and fit for purpose.
Development of a simulation scenario is
straightforward process, which does not
require programming skills and a lot of
intellectual effort.
• Benchmarked against other industry
standard software products and tested in
real operations environment
MineDES Benchmarking and Validation
600 m 0 5 10 15 20 25 30 35 40 45 50 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 MineDES TALPACTruck dynamics model benchmarking against
industry standard software
T
r
u
c
k
s
p
e
e
d
(
k
m
/
h
)
Distance (km)
Testing against real truck data.
113.6 113.8 114 114.2 114.4 114.6 114.8 115 115.2 115.4 17 17.2 17.4 17.6 17.8 18 18.2 Y ( k m )GPS real tr uck data plot:
0 5 10 15 20 25 30 35 40 0 5 10 15 20 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
Truc k gr oun d sp eed pr ofi le. GPS vs. MineDES
Truc k t ravel dis tance pro fil e. GPS vs. MineDES
MineDES model - design view:
Experiment results:
T r u c k s p e e d ( k m / h ) D i s t a n c e ( k m )
