colin eustace, aurecon - bmh simulation – a cornerstone of fit-for-purpose design
DESCRIPTION
Colin Eustace, Associate, Resources & Manufacturing Services, Aurecon delivered this presentation at the 10th Annual Bulk Materials Handling conference 2013. This conference is an expert led forum on the engineering behind the latest expansions and upgrades of bulk materials facilities. It also evaluates the latest engineering feats that are creating record levels of throughput whilst minimising downtime. For more information on this conference, please vist http://www.informa.com.au/bmh2014TRANSCRIPT
Bulk Materials Handling Week
Simulation – A Cornerstone of Fit-for-Purpose Design Colin Eustace, Simulation Technical Lead, Aurecon
June 2013
3
Bulk Materials Handling Week 2013
Simulation – A Cornerstone of Fit-for-Purpose Design
• Topic: The case for simulation of BMH
systems
• Focus: Do I need simulation?
What does simulation actually achieve?
Identification of situations where
simulation analysis is important and
possibly impacts on design.
• Context: Supply chains for bulk
materials (coal and iron ore, aggregate)
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What is the benefit of simulating?
• Why would you consider simulation?
• What do I need to know about my problem
to understand whether simulating is
worthwhile?
Worthless? Valuable?
Availability of information
Interfaces with other operations
Operational Complexity
Variability, Queuing
behaviour
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Delivery of specific value
• Value should be able to be identified
• Value is in refining assumptions used in a static
model
• Assumptions may change significantly affecting
throughput and performance estimates
• Assumptions may stay the same, reducing uncertainty
in performance estimates
• One of the best ways of identifying the value that
simulation provides is by structuring the simulation
analysis around an extension of a static model
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Accura
cy o
f outp
ut
Level of effort
Static Model
Dynamic
Model
• Our objective is to improve on the
accuracy/ robustness of the static
models that we already have
• Many simulation projects fail to do this
Static vs. Dynamic Models
Dynamic
Model
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Comparison with static calculations
• A line-by-line comparison should always be
possible if both the static model and
simulation model are well structured
• Static calculations include:
• Parameters related to equipment specs
• Abstracted/ averaged performance
assumptions
• Rules of thumb/ educated guesses
(availability at interfaces, typical delays)
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Coal In-loading Example
Symbol Units Rate A Rate B Rate C Formulas
C tph 8,000 8,700 9,500
PT t 120 Wagons
PW t
LW m
vT km/hr 1.62 1.76 1.92 vT = C.L / 1000.PW
ɳP %
ɳR %
ρRP %
ɳT %
tA hrs tA = 8760.ɳT
tLA days tLA = 365 - 365.ɳT
i no.
RC %
RS %
RSC %
RR %
RT % RT = RCi.RS.RSC.RR
d1 m
vC m/s
d2 m
vS m/min
tOD min
tDR min
LLS m
LLM m
d3 m
t1 hrs 1.29 1.19 1.09 t1 = PT / C.RT
t2 hrs 0.03 0.03 0.02 t2 = LLM / 1000.vT
t3 hrs 1.32 1.22 1.11 t3 = t1 + t2
t4 hrs 0.29 0.29 0.29 t4 = 0.5.d2 / 60.vS
t5 hrs 0.07 0.07 0.07 t5 = (d1 - 0.5.d2) / 3600.vC
t6 hrs 0.25 0.25 0.25 t6 = tOD / 60
t7 hrs 0.0920 0.09 0.09 t7 = (LLS + d3) / vT + tDR / 60
t8 hrs 0.70 0.70 0.70 t8 = sum(t4:t7)
t9 hrs 1.93 1.83 1.74 t9 = PT / C + t2 + t8
t10 hrs 2.03 1.92 1.82 t10 = t3 + t8
G1 tph 4,737 4,999 5,281 G1 = PT / t10
C1 Mtpa 38.8 41.0 43.3 C1 = G1.tA
ɳE %
t11 hrs 2.38 2.26 2.14 t11 = t10 / ɳE
G2 tph 4,026 4,249 4,489 G2 = PT / t11
tLR days 18.7 18.3 17.6 tLR = tA.(t1 - t1.RT) / 24.t10.ɳe
t12 min 21.5 20.3 19.2 t12 = (t11 - t10)/60
p1 % 17.6 18.6 19.7 p1 = t12 / t9
p2 % 18.5 19.5 20.6 p2 = t12 / t10
C2 Mtpa 33.0 34.8 36.8 C2 = G2.tA
Days Lost to Rel iabi l i ty
Average Additional Delay - Tra in Unavai lable
Proportion of Minimum Tra in Turnaround Time
Proportion of Average Tra in Turnaround Time
Realistic Annual Capacity
Stacker Long Travel Speed 40
Operator Setup Delays for Stacker 15
Inloading Operation Cycle Parameters
Average Conveying Dis tance to Stockyard Mid Point 2,000
Conveyor Belt Speed 5.1
Availability - Planned Maintenance and Weather
Planned System Avai labi l i ty - Port
Average Stockyard Runway Length 1,400
Days Lost to Planned Maintenance and Weather
Sticky Coal Loss Factor 97.0
Rai l Rel iabi l i ty 99.0
Tota l Inloading System Rel iabi l i ty 92.7
Average Number of Inloading Conveyors 3
Conveyor Rel iabi l i ty 99.5
Stacker Rel iabi l i ty
Average Single Wagon Payload 80.0
Wagon Length Coupl ing to Coupl ing 16.2
Unloading Tra in Speed
Parameter
Train Parameters
Inloading Capaci ty
Average Single Tra in Payload 9,600
Trains Based on Realistic Availability
Effective Tra in Avai labi l i ty 85.0
Average Tra in Turnaround Time
Gross Unloading Rate
Minimum Tra in Turnaround Time
Average Tra in Turnaround Time
Trains Always Waiting - System Choke Fed
Gross Unloading Rate
Maximum Annual Capacity
Average Time to Repos i tion Stacker
Time to Run Out Coal
Operator Setup Delays
Tra in Delay at Start of Dumping
Minimum Gap Between Tra ins
Locomotive Delay Mid Tra in
Stop Dis tance Beyond Station 50
Inloading Operation Cycle Time Components
Time to Discharge Coal incl . Rel iabi l i ty
2
Length of Locomotives - Start of Tra in 45
Length of Locomotives - Middle of Tra in 45
97.0
Reliability - Unplanned Downtime
23.5
Planned System Avai labi l i ty - Ra i l 95.0
Proportion of Maintenance Scheduled to Coincide 50.0
98.0
ɳT = min(ɳP,ɳR) * *max(ɳP,ɳR)
+ ρRP.(100 - max(ɳP,ɳR)]Tota l Planned System Avai labi l i ty 93.58
Avai lable Loading Time incl . Avai labi l i ty 8,197
Tota l Time to Empty Tra in
Average Driver Reaction Delay at Start of Tra in
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Cases where Simulation is not required
• No significant variability
• Sufficiently buffered from external influences
• No interactions between concurrent
processes or shared equipment
• When visualisation is of little benefit
• When there is insufficient information
available
Availability of information
Interfaces with other operations
Operational Complexity
Variability, Queuing
behaviour
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Borderline cases
• Even when the system exhibits queuing
behaviour (e.g. coal terminal) the value of
simulation may be questionable in some
cases
• No shared resources
• Concept level analysis
• Little detailed information
• No complex constraints
Availability of information
Interfaces with other operations
Operational Complexity
Variability, Queuing
behaviour
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Cases where industry rules of thumb are likely
to be the best indicator
• No quantitative description of the operation
is available
• Events are infrequent/ unusual
• Complicated to implement and difficult to
develop a reliable representation
13
Cases where simulation is essential
• Always application specific
• Variability in rates or availability
• Influenced by external operations (train
availability)
• Interactions between concurrent
processes or shared equipment
Example: Large
Aggregate Import
Terminal
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Aggregate Import Terminal Example: Layout
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Static Capacity Analysis
• Investigate various aspects of the terminal
• Assume average digging rate across the
ship of 50% of peak digging rate
• Assume crane utilisation of 80% at capacity
• Capacity targets appear to be achievable
• Award contract for detailed design and
construction
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Dynamic Capacity Analysis
DWT 1 2 3 4 5 6 7
62902 4 4 3 5 1 1 2
42400 1 1 2 2 2
60046 2 3 3 2 2 3 1
37350 1 1 1 2 1
93534 2 1 3 3 1 2 2
37350 1 1 1 2 1
41000 1 3 3 3 2
42400 2 1 2 1 2
64778 2 1 1 2 1 2 3
54656 1 1 1 1 1 1 1
53676 1 3 2 3 2 3 2
81004 1 1 2 1 1 1 1
53074 2 2 3 3 4 1 1
37150 2 2 2 1 2
62944 2 2 1 2 2 1 2
54446 4 2 2 1 1 3 3
37000 1 1 2 3 3
52906 2 2 1 2 2 1 2
Hatch Number
• Considers unloading sequences and
constraints for concurrent events
• Uses historical data as a guide for likely ship
load plans
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Dynamic Capacity Analysis
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Capacity Analysis Outcomes
• Average crane utilisation more than ~50% is
difficult to achieve with the current
configuration
• Capacity is about two-thirds of the target
• Re-think the design?
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Not all simulation models are the same
• Each simulation model is targeted at a
different level of abstraction
• Different models are capable of doing a
different range of analysis
Example: Coal Export
Terminal Simulation
Model Approaches
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R
S/R
S/R S/R S/R
S/R
S S S S
S/R
S/R
Different Approaches – Coal Terminal
Example
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S/R
S/R S/R
S/R S/R
S/R
Material Flow Transactions 10,000t
10,000t
80,000t
70,000t
Approaches to Simulating Bulk Export
Terminals 101
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S/R
S/R
S/R
S/R
S/R
S/R
Approaches to Simulating Bulk Export
Terminals 101
• Stockpile “Bins” and equipment pools are
often used to approximate stockpile
geometry
25
Examples – Cargo Assembly
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For many more complex BMH operations, simulation really
is important to ensure the system works
• Consider using simulation early to improve design rather
than checking at detailed design to see where the
established problems areas are
• Think about whether there is anything that warrants
simulation (queuing, variation, shared equipment…)
• Tie detailed simulation closely to preceding static
analysis
• Simulation must ADD VALUE to static analysis without
diminishing any of the detail
• Tailor the approach to the desired outcomes (what
assumptions are we uncertain about)
Concluding Remarks
Benefitting from Thorough Analysis