Bulk Materials Handling Week

Simulation – A Cornerstone of Fit-for-Purpose Design Colin Eustace, Simulation Technical Lead, Aurecon

June 2013

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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

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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

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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