The Need for Near Real-Time Hydraulic Modelling

The Need for
Near Real-Time Hydraulic
Modelling
Kevin Henderson – Network Planning Manager
Bristol Water – Key Statistics
 Length of mains – 6,670 km
 512,000 consumers – 410,000 service connections
 Average age of Network: approx. 60 yrs
 Mains material predominately cast iron, plastic and asbestos cement
 Average zone pressure 45mH
 Number of treated water storage reservoirs – 139 (total storage capacity 533Ml)
 Number of pumping stations – 164
 Resilient, flexible, yet complex, operating scheme
 Integrated distribution system; ring main for bulk transfer
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Bristol Water – Leakage Management
 Comprehensive network sectorisation (DMAs – 376; sub-DMAs – 1,500; property coverage 99%)
 Continuous data logging through telemetry/SCADA
 Advanced level of Pressure management schemes (over 400 pressure control zones)
 Low leakage levels and good performance, (NRW levels 14%, 70 l/prop/day, ILI 1.7)
 Advanced leakage management and reporting systems.
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Bristol Water – Network Planning
Small Team of Network Modelling Engineers, Network Modelling Assistants, and Technicians
 Network Modelling, Long and Short Term Planning, Flushing Programmers, Mains Condition
Assessments, Mains Rehab Prioritisation
 Detailed All-Mains models for each Treatment Works Zone calibrated down to DMA Level
 Long Term Planning
– Periodic Reviews assessing storage requirements, Security of Supply (Critical Mains and
Storage/System Capacity)
 Short Term Planning
– Operational Support for current AMP 5 engineering works has been challenging
– Incident Support
 Other Model Usage:
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– DMA / PRV Design
– Power Optimisation
– Poor pressure investigations
– Critical Pipe Analysis
– Water Quality Improvement
– Developer Enquiries
Why Near Real-Time Modelling?
 Ideally all mains model running continually and linked to telemetry points:
– Flow and Pressure, High-Speed Pressure data, Reservoir Levels, Chlorine Residuals, Turbidity
Monitoring, Power.
– Customer Contacts (via GIMS).
– Water Quality Analysis.
– Etc.
 Immediate benefits include:
– An audit of flow meters and pressure / level transducers.
– On-going validation ensures reliable systems performance data back from the field, and continual
validation of model(s).
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Why Near Real-Time Modelling?
 Greatly enhanced Operational Management & Support
– Hydraulic reliability and leakage management.
– Fine-grain control over the state of the system leading to greater efficiency and cost savings.
 Incident Management (Questions asked during an incident include):
– How many customers are likely to be affected? Where are the vulnerable customers?
– How long before reservoir(s) X empties?
– How long before the system drains down? What order are supplies going to be lost in?
– What rezones are possible? Risk assessment needed?
– How long will it take to recharge the system? What is the optimal sequence?
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Why Near Real-Time Modelling?
 Current modelling techniques are good, but can take up to 2 hours to prepare a large model once an
incident is called
 Real-Time Modelling will:
– Potentially provide warning of impending problems
– Potentially monitor and control system valves and pumps?
– Potentially reduce power consumption whilst maintain resource requirements?
– Enable immediate response to incidents
– Enable endless “what if” scenarios to be modelled for contingency purposes!
 STREAM Project with Imperial College
 IW Live and BalanceNet Trial
 Possible training tool for control room operators
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Data Flows
GPRS Loggers (District / PRV Zones)
920 Flow Points
465 Pressure Points
Reservoir Levels
103 Points
InfraSense TS
High Speed Logging
35 loggers
Water Quality
46 Turbidity Points
68 Chlorine Residual Points
73 Other Points
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Available Data
Weather Monitoring
14 Temperature Points
7 Rain Gauges
Operational Sites
400 Flow Points
280 Pressure Points
Power Monitoring
50 points
System Operation
320 Valve Statuses
22 Pump Frequency / RPMs
320 Pump Statuses
Data/Model Assembly & Maintenance
Telemetry Archive
(15 minute updates)
GPRS Logging
(6hr / 24hr)
Ad Hoc InfraSense
High Speed
Logging
Telemetry
(Flow / Pressure
Validation)
Customer Contacts
(GIMS)
+
Water Quality Data
(LIMS)
GIS
Network Models
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Operational
Planning
Real Time Models
Mobile GIS
Network Operations
(Valve Shutting etc.)
Why Near Real-Time Modelling?
GIMS
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nRT Modelling Case Study #1:
AMP 5 Trunk Mains Lining
Case Study 1 – Potential to Provide Operational Alerts or Control Key Valves
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nRT Modelling Case Study #1:
AMP 5 Trunk Mains Lining
First test with Reservoir Inlet shut and EOV at 20%
EOV throttled as
Reservoir Inlets shut
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nRT Modelling Case Study #1:
AMP 5 Trunk Mains Lining
Pressure increase in Old Market district of Approx. 12m
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nRT Modelling Case Study #1:
AMP 5 Trunk Mains Lining
Burst Data in GIS
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nRT Modelling Case Study #1:
AMP 5 Trunk Mains Lining
Second test with res inlets shut and EOV adjusted as pressures rise
EOV throttled even
further as pressures rise
EOV throttled as
Reservoir Inlet shut
EOV throttled further
at night
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Similar profile on
2nd night
nRT Modelling Case Study #1:
AMP 5 Trunk Mains Lining
Further results of second test, with EOV correctly opened
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nRT Modelling Case Study #2:
Preventative Maintenance
Case Study 2 – Potential Preventative Maintenance
Treatment works &
High Lift pumps
Trunk main to be
slip-lined
Temporary
Overland Main
Service Reservoir
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nRT Modelling Case Study #2:
Preventative Maintenance
Flow through overland main with valve only 5% open
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nRT Modelling Case Study #2:
Preventative Maintenance
Pump Performance at a flow of approximately 2 Ml/d
Pump efficiency is 43%, with valve only 5% open
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nRT Modelling Case Study #2:
Preventative Maintenance
Flow through overland main with valve only Fully open
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nRT Modelling Case Study #2:
Preventative Maintenance
Pump operating further down its curve, at higher flow and higher speed
Pump efficiency is 51%, with valve fully open
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nRT Modelling Case Study #3:
Incident Management
Case Study 3 – Major Incident (25/Sep/2014)
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nRT Modelling Case Study #3:
Incident Management
Pipe Burst
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nRT Modelling Case Study #3:
Incident Management
System Schematic
Field Lab Area
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nRT Modelling Case Study #3:
Incident Management
High Speed Data (InfraSense TS) – Within Distribution Network
Real-Time Monitoring of the System Hydraulics
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nRT Modelling Case Study #3:
Incident Management
EOV Operation / Pipe Location
Approximate length of failure
30” Ferrous Pipe,
laid over PSC
Pre-stressed
concrete Storm
Drain
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Conclusions
 Significant advances in sensing and control
 Higher spatial and temporal resolution of hydraulic data
 Computationally efficient and robust hydraulic solvers
Near Real-Time Hydraulic Modelling is becoming
affordable and also critical to support smarter & efficient network operation
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Thank you
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