Applying Resilience Modelling Tools to the Design of ... Resi… · Applying Resilience Modelling...
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Keng Han Tng 1 , Chloe Quinn 1 , Laura Onyango 2 , James Woods 2 , Yuan Wang 1 , Craig Roberts 3 and Greg Leslie 1 Applying Resilience Modelling Tools to the Design of Membrane Plants for Pathogen Removal Introduction • Resilience is a system’s ability to maintain routine function even under unexpected circumstances. • It is an essential factor in ensuring continuous process throughput whilst remaining compliant with strict water discharge guidelines. • Resilience modelling tools have been widely used in the Petrochemical, Oil and Gas, and Aviation industries to model process reliability and safety over the last 15 years. • No standard resilience modelling method has been developed for the water treatment industry. Objectives • Establish a mechanical resilience model for water recycling systems. • Quantify process resilience and predict process equipment failure using resilience model. • Develop “What-if” scenarios for resilience model’s sensitivity analysis tests. OPTAGON Model Equipment Resilience Process Resilience System Resilience Step 1: Data Sourcing and Collection Resilience Model • Adopting a resilience modelling tool from the Oil and Gas industry, GL Noble Denton’s (GLND) OPTAGON Simulation Package was chosen. • OPTAGON is GLND’s Monte Carlo-based Reliability, Availability and Maintainability (RAM) simulation tool which is capable of modelling the performance of asset. • With user-variated real-time data, OPTAGON is able to accurately predict equipment failure and system resilience. Asset Resilience Step 2: Data Analysis and Mapping Step 3: Resilience Modelling & Sensitivity Analysis Methodology System Level Process Level Equipment Level Design Reliability Redundancy Maintenance Spares Equipment Performance Equipment Availability Equipment Resilience Process Availability Process Performance Process Resilience System Resilience System Availability System Performance Data Element Element Description Source Documentation Plant Level Process configuration/Design Overall process design diagram/information of plant. Engineering drawings and operational documents (P&ID, PFD, O&M Manuals) System Compliance Constraints Plant quality compliance thresholds. Compliance related documentation, Alarm thresholds Equipment Level Equipment name The common name for the equipment set Asset Register, equipment data sheets, Engineering drawings and operational documents such as P&ID, PFD, O&M Manuals Equipment ID The unique identifier for the equipment Asset Register, equipment data sheets, Engineering drawings and operational documents such as P&ID, PFD, O&M Manuals Number of, duty, redundancies and spares Number and operating mode of the equipment set, ie duty, assist, standby, boxed spare etc. Engineering drawings and operational documents such as P&ID, PFD, O&M Manuals, layout drawings Equipment Design Capacity (i.e. Flow) Design capacity of the equipment in terms of primary flow (m3/h). Equipment data sheets, OEM documentation, Historic flow monitoring data (avg), and Process Flow Diagrams Mean Time Between Failure (MTBF) Mean Time Between Fail (MTBF) for each failure mode if identified. Equipment failure rates, site maintenance records from CMMS. Ideally records provided should cover full operating period. Mean Time To Repair (MTTR) Mean Time To Repair (MTTR) for each failure mode if identified. Site maintenance records from CMMS, replacement lead time from suppliers, and maintenance regime documentation. Maintenance Frequency Number of planned maintenance activities per year O&M Manuals Maintenance Duration Duration for the planned maintenance. O&M Manuals • Equipment failure and performance data is sourced from 7 water recycling plants worldwide. • Relevant information is collected from a wide array of data sources. • Cataloged equipment data is sorted and mapped according to process equipment specified in the model (Reference Plant). • Equipment arranged with design and operational capacities based on functional location. • Operation & Maintenance (O&M) Manuals provide vital information on equipment availability. • MTBF and MTTR are also calculated if not previously provided. • Equipment criticality is determined based on failure and maintenance data. Conclusion Shutdown Shortfall (ML) 0 10000 20000 30000 40000 50000 60000 0 50 100 150 200 250 300 350 Throughput (ML) Time (days) Maximum Plant Capacity Operational Availability Annual Shutdown Maximum Potential Annual Production Actual Annual Production Shortfall (ML) 0 50 100 150 200 250 Base Case Disc Filter - Boxed Spare Aeration Blower - Fails every 2 years P Dosing Pump - Boxed Spare Annual RAS Blockage Fixed within 24 hours 3 RAS Pumps fail (Breach within 3h) Raw Sludge Press Fails twice per month Final Press Fails twice per month Digester Feed Pump - 2 Stocked Spares No. of non-compliant events per 100 years Base Case 116 events Resilience Improves (i.e. fewer consent breaches) Resilience Worsens (i.e. more consent breaches) • Mapped data becomes input variables for OPTAGON to model asset’s mechanical resilience. • Monte Carlo simulations of 10,000 realisations ensure confidence of modelling results. • Results also demonstrate equipment interdependency. • Modelling results would quantify the asset’s overall reliability and resilience. • Sensitivity analysis would further highlight which input variable would have the greatest impact on the system. • “What-if” scenarios would test the robustness of the reference plant and aid with process optimisation. Resilience = (Availability, Performance) Availability = (Reliability, Maintainability) Risk = (Likelihood, Consequence) Acknowledgements • OPTAGON can model complex water recycling systems with high level of accuracy and consistency. • Modelling results would be able to quantify asset resilience, criticality and risk. • Resilience modelling can predict and improve asset performance throughout asset’s lifespan. • Sensitivity analysis would support asset management decisions and aid in efficiency and profitability. • Reference model can also be used to provide insight to specific failure modes and resulting effects.
Transcript of Applying Resilience Modelling Tools to the Design of ... Resi… · Applying Resilience Modelling...
Keng Han Tng1, Chloe Quinn1, Laura Onyango2, James Woods2, Yuan Wang1, Craig Roberts3 and Greg Leslie1
Applying Resilience Modelling Tools to the Design of Membrane Plants for Pathogen Removal
Intr
od
uct
ion
• Resilience is a system’s ability to maintain routine function even under unexpected
circumstances.
• It is an essential factor in ensuring continuous process throughput whilst remaining
compliant with strict water discharge guidelines.
• Resilience modelling tools have been widely used in the Petrochemical, Oil and Gas,
and Aviation industries to model process reliability and safety over the last 15 years.
• No standard resilience modelling method has been developed for the water
treatment industry.
Ob
ject
ive
s • Establish a mechanical resilience model for water recycling
systems.
• Quantify process resilience and predict process equipment
failure using resilience model.
• Develop “What-if” scenarios for resilience model’s sensitivity
analysis tests.
OP
TAG
ON
Mo
de
l
Equipment Resilience
Process Resilience
System Resilience
Step 1: Data Sourcing and Collection
Re
silie
nce
Mo
del
• Adopting a resilience modelling tool from the Oil and Gas
industry, GL Noble Denton’s (GLND) OPTAGON Simulation
Package was chosen.
• OPTAGON is GLND’s Monte Carlo-based Reliability,
Availability and Maintainability (RAM) simulation tool which is
capable of modelling the performance of asset.
• With user-variated real-time data, OPTAGON is able to
accurately predict equipment failure and system resilience.
Asset Resilience
Step 2: Data Analysis and Mapping
Step 3: Resilience Modelling & Sensitivity Analysis
Meth
od
olo
gy
System Level
Process Level
Equipment Level
Design Reliability Redundancy Maintenance Spares
Equipment Performance
Equipment Availability
Equipment Resilience
Process Availability
Process Performance
Process Resilience
System Resilience
System Availability
System Performance
Data Element Element Description Source Documentation
Plant Level
Process configuration/Design Overall process design diagram/information of plant. Engineering drawings and operational documents (P&ID, PFD, O&M Manuals)
System Compliance Constraints Plant quality compliance thresholds. Compliance related documentation, Alarm thresholds
Equipment Level
Equipment name The common name for the equipment set Asset Register, equipment data sheets, Engineering drawings and operational documents
such as P&ID, PFD, O&M Manuals
Equipment ID The unique identifier for the equipment Asset Register, equipment data sheets, Engineering drawings and operational documents
such as P&ID, PFD, O&M Manuals
Number of, duty,
redundancies and spares
Number and operating mode of the equipment set, ie duty, assist,
standby, boxed spare etc.
Engineering drawings and operational documents such as P&ID, PFD, O&M Manuals,
layout drawings
Equipment Design
Capacity (i.e. Flow)
Design capacity of the equipment in terms of primary flow (m3/h). Equipment data sheets, OEM documentation, Historic flow monitoring data (avg), and
Process Flow Diagrams
Mean Time Between Failure
(MTBF)
Mean Time Between Fail (MTBF) for each failure mode if identified. Equipment failure rates, site maintenance records from CMMS. Ideally records provided
should cover full operating period.
Mean Time To Repair
(MTTR)
Mean Time To Repair (MTTR) for each failure mode if identified. Site maintenance records from CMMS, replacement lead time from suppliers, and
maintenance regime documentation.
Maintenance Frequency Number of planned maintenance activities per year O&M Manuals
Maintenance Duration Duration for the planned maintenance. O&M Manuals
• Equipment failure and performance data is
sourced from 7 water recycling plants worldwide.
• Relevant information is collected from a wide array
of data sources.
• Cataloged equipment data is sorted and
mapped according to process equipment
specified in the model (Reference Plant).
• Equipment arranged with design and
operational capacities based on functional
location.
• Operation & Maintenance (O&M) Manuals
provide vital information on equipment
availability.
• MTBF and MTTR are also calculated if not
previously provided.
• Equipment criticality is determined based on
failure and maintenance data.
Co
ncl
usi
on
0
10000
20000
30000
40000
50000
60000
0 50 100 150 200 250 300 350
Th
rou
gh
pu
t (M
L)
Time (days)
Maximum Plant Capacity Operational Availability
Annual
Shutdown
Maximum Potential Annual Production
Actual Annual ProductionShortfall (ML)
0
10000
20000
30000
40000
50000
60000
0 50 100 150 200 250 300 350
Th
rou
gh
pu
t (M
L)
Time (days)
Maximum Plant Capacity Operational Availability
Annual
Shutdown
Maximum Potential Annual Production
Actual Annual ProductionShortfall (ML)
0
50
100
150
200
250
Base Case Disc Filter -
Boxed Spare
Aeration
Blower - Fails
every 2 years
P Dosing
Pump - Boxed
Spare
Annual RAS
Blockage
Fixed within
24 hours
3 RAS Pumps
fail (Breach
within 3h)
Raw Sludge
Press Fails
twice per
month
Final Press
Fails twice
per month
Digester
Feed Pump -
2 Stocked
Spares
No
. o
f n
on
-co
mp
lian
t even
ts p
er
100 y
ears
Base Case
116 events
Resilience Improves (i.e. fewer consent breaches)
Resilience Worsens (i.e. more consent breaches)
• Mapped data becomes input variables for OPTAGON to
model asset’s mechanical resilience.
• Monte Carlo simulations of 10,000 realisations ensure
confidence of modelling results.
• Results also demonstrate equipment interdependency.
• Modelling results would quantify the asset’s overall
reliability and resilience.
• Sensitivity analysis would further highlight which input
variable would have the greatest impact on the system.
• “What-if” scenarios would test the robustness of the
reference plant and aid with process optimisation.
Resilience = (Availability, Performance)
Availability = (Reliability, Maintainability)
Risk = (Likelihood, Consequence)
Ackn
ow
led
gem
en
ts
• OPTAGON can model complex water recycling systems with high level of accuracy and consistency.
• Modelling results would be able to quantify asset resilience, criticality and risk.
• Resilience modelling can predict and improve asset performance throughout asset’s lifespan.
• Sensitivity analysis would support asset management decisions and aid in efficiency and profitability.
• Reference model can also be used to provide insight to specific failure modes and resulting effects.
