“Good models are more than screen deep” Newmont Boddington Gold Case Study
Bulk Materials Handling 2014 conference 01-05
30th April 2014
© 2012 TSG Consulting All Rights Reserved Simulation | Optimisation | Strategic Planning
1 • CAPEX Optimsation
2 • Plant commissioning
performance evaluation
3 • Operating &
maintenance optimisation
Three recent & succesful NBG studies
Developing confidence in the NBG DES
model
Site knowledge NBG
Model integrity TSG
Cross functional
study team
System response
System insights
Iterative learning
Introduction
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© 2012 TSG Consulting All Rights Reserved Simulation | Optimisation | Strategic Planning
TSG and NBG | study timeline
The following represents the different studies that have been conducted over the past 14 years at the start date of the study.
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Timeline 2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14
Preliminary study carried
out to evaluate initial flow
sheet. Plant only model
Model updated to represent
new flow sheet
Model expanded to
include mining to undertake
trucking evaluation
Mine interface study
Contribution to financial modeling
Model update and additional
scenario analysis
Expanded works study
Optimization study
Mine plant interface study
with revised equipment and
operating strategy
Bottleneck analysis, upgrade
options and variability evaluation
Operations modeling
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Model animations
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Detailed truck and shovel model implemented during the latest studies Enabled understanding of crusher utilisation and variability in supply of material – proper understanding of coarse ore stockpile sizing and the impact on system productivity
NBG mining operation | animation
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NBG mining operation | animation
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Process model was updated from previous work – major capital options testing required the largest model updates Significant effort to insert detailed bin level controls, up-to-date failure data, smart screen splits, conveyor capacity limits
NBG processing plant | animation
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NBG processing plant | animation
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Summary of results
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CAPEX Optimisation | summary
Option 1: Minor CAPEX process improvement options Option 2: Large CAPEX possibilities allowing higher mill production rates Iterative process to arrive at a final set of cases – increasing levels of system response insights and new improvement ideas
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Process improvements capital options at varying mill rate assumptions
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Plant commissioning performance evaluation | summary
Detailed mining model development enabled rehandle approaches to be tested Confirmation and optimisation of truck and shovel strategies supplying the processing facility
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Mining truck and shovels optimisation Effect of equipment upgrades with and without
an operating mine
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Operating & maintenance optimisation | summary
Testing of maintenance strategies to minimise system disturbances and maximise production More than 15 maintenance schedules tested Minor upgrade timing effects
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Planned maintenance scenarios testing Timing of equipment upgrades
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NBG expertise
Significant expansion project management and site management experience. This is a key element to a successful study outcome
Data extraction and preparation: NBG team members excelled during this phase of the project and enabled model validation Documentation trail for internal critique
Understanding of the decision making processes whether they be automated or manual decisions. One team member had significant control engineering experience on site
Understanding how the supply chain should respond to change. Very knowledgeable team members with substantial operations experience. This enabled detailed validation work
TSG expertise
TSG has conducted more than 500 supply chain studies in almost 3 decades and is the company’s core business. Successful studies require a high level of attention to detail for both modelling and reporting of pertinent insights
Optimal input of data into the DES model and highlighting areas where data quality is key to a successful outcome Data and logic document implemented for the model
Iterative and collaborative work with the NBG team to ensure model functionality and model output tools were accurately reflecting the real system
Assisting with the development and implementation of sensitivity cases . Displaying meaningful outputs to allow system level assessment
DES model performance | developing confidence
Collaborative effort to arrive at a quality study outcome
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NBG assigned significant resourcing to validate model against known plant performance Detailed checking of sensitivity case model outputs built confidence in expansion option case results
DES model performance | example outputs
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• System component KPI’s • Annual production • Equipment states • Average equipment rates or truck cycle times
• Equipment output time series & distributions
Conveyor rate limit
3, 2, 1 & 0 units offline Major planned outage
No feed to conveyor 03
Average ball mill performance for a “1-year” model run
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• System response testing • System productivity with increasing mill rates
(see right) • System productivity with increasing truck
numbers • System productivity with increasing COS size
• Highlight point at which upgrades are no longer cost effective • For the system as defined • Other model changes could shift the point of
cost effectiveness • Combinations of upgrades are often effective • Use of detailed outputs to assess where
bottleneck predominates – then propose changes to equipment upgrades or control strategies
DES model performance | system response
System response evaluation provides another layer of confidence that model is performing as expected “Good models are more than screen deep”
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32
33
34
35
36
37
38
Mean Mill Rate -‐300
Mean Mill Rate -‐200
Mean Mill Rate -‐100
Mean Mill Rate Mean Mill Rate + 100
Mean Mill Rate + 200
Mean Mill Rate + 300
Mean Mill Rate + 400
Mean Mill Rate + 500
Mill Rate Sensitivity
Mtpa
Expected diminishing system return from increasing mill rate
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-1 -0.5 0 0.5 1 -1
0
133
34
35
36
37
38
Thro
ughp
ut (M
tpa)
• DES modelling a powerful tool to understand supply chains
• NBG and TSG efforts resulted in a robust supply chain model • Provides confidence for capital upgrade
decision making • Directly supplies financial analysis (IRR
determination)
• Study process generates improvement ideas • Facilitates a system level understanding • Can highlight shortfalls in data collection
systems • Can highlight areas where higher system level
control strategies can be improved
Conclusions | “Good models are more than screen deep”
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Design of Experiments: efficient system response understanding
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Questions
The end
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Appendix | DES general description
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Dynamic Modelling • Predicts how a system performance will vary with time. • 1 minute time steps typically (525,600 minutes = 1 year)
Monte Carlo Simulation (random numbers) • Variability is incorporated in events such as breakdowns
and failures, grade fluctuations, load sizes. • Combining the randomness of each operation through
modelling enables us to quantify the expected variation of the physical system and to predict performance with a level of confidence.
Discrete Entities and Processes • Each physical item (truck, mill, pump, ship) is modelled
as a discrete element with its own uniquely defined set of properties or attributes (speed, operating rate, reliability, carrying capacity).
• Rules are defined to describe the interaction of the entities in the system.
• The models are animated.
Discrete event simulation (DES) | general description
DES promotes system understanding and is inclusive of system input variability – results fall on a distribution DES has three defining elements:
Sub Process
Sub Process
Sub Process
Sub Process
Sub Process
Data
Model Boundary
Sub Process
Sub Process
Queue
Surge Pile
Delay
?
Operational Decisions
Dependent Processes
Logic
Understanding Inputs
Feedback
Variation
Performance