the data layers in the SWAN Forum - Smart Water Networks Forum - presented by Haggai Scolnicov, TaKaDu's CTO

1. How to build a Smart Water Network
A fictional case study
Haggai Scolnicov
CTO, TaKaDu Ltd.

2. Goals
• Introduce layered model of Smart Water Networks
• Discuss current and future evolution of SWNs
• How smart is “smart”? Numbers and notions
• Mention TaKaDu enough to satisfy our VP Marketing
• Hollywood ending
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3. Meet Mr. Waters
Mr. Waters, VP Water Supply at Typical Water Co., has
decided to make his network smart!
Why?
- Increase efficiency
(water, energy, manpower, $$$)
- Be in control and proactive
- Leverage existing IT and skills
- Improve customer service
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4. What is a Smart Water Network?
And what is it good for?
Data-driven components to help manage and
operate the physical network of pipes etc.
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5. What is a Smart Water Network?
And what is it good for?
Data-driven components to help manage and
operate the physical network of pipes etc.
Monitoring Reporting
& alerts
Decision
support Evidence-based
planning
Feedback &
Remote
Automation
control
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6. Pipe dreams
The physical network of Typical Water Co.
• 100,000 service connections (25Mm3/yr)
• 1000 km of distribution mains
• Some trunk mains, reservoirs, pumps, PRVs
• Many valves and hydrants
And probably much more…
• Built for monitoring? Some DMAs, PMAs…
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7. Taking the cake in SWN technology
The physical layer may have many clever
inventions in it – but they don’t make it
“smart” or more data-enabled
We need sensors!
Physical layer
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8. Senseless?
The sensing and control layer
• Hundreds of sensors + loggers on mains
(e.g. flow & pressure in DMAs + installations)
• Supply metering exists!
• AMR? Or stick with monthly manual?
• Acoustic? Pipe inspection? Mobile loggers…?
Investment in sensors:
$100-5000 a year for total cost of ownership
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9. Taking the cake in SWN technology
Data just piles up at multiple, remote data
loggers. It is collected and used in models
and planning a few times a year. It is usually
too stale for operational use.
We need telemetry!
Sensing and control
Physical layer
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10. Failure to communicate?
The communications layer
• SCADA (exists on production-side)
• Transmit every day? 4 hours? 15 minutes?
(Conserve communications costs, battery longevity…)
• Sample and log every 15 minutes
• Remote control for valves? Pumps?
• High frequency sampling to see transients?
Investment in telemetry:
$X M + $500 per sensor per year ?
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11. Taking the cake in SWN technology
Operators see data from hundreds of sources and control devices
remotely. Some real-time usage! But how to organise and inspect
all this data? And what about data from other systems (like asset
management)?
We need data management!
Collection and communication
Sensing and control
Physical layer
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12. Data, data, everywhere
The data management and display layer
• Off-the-shelf and tailored applications:
– Network data applications (operator / analyst)
– Water loss monitoring
– Hydraulic modeling and network planning
– Evidence-based reporting…
• Interfaces with GIS, Asset Management,
Workforce Management…
• Some rule-based automation!
• Applications reach multiple departments
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13. Taking the cake in SWN technology
Analysts and engineers use recent sensor data through systems
But is this the best we can achieve? This was supposed to
tailored to water distribution, together with workforce, asset, and
be a smart network. With all these computers, couldn’t they
other data, routinely initiating timely action. Reporting and
help my staff make sense of the data?
planning made easier and better grounded in fact.
Data management & display
Collection and communication
Sensing and control
Physical layer
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14. Drowning in numbers?
The data fusion and analysis layer
• Real-time data-driven pressure management
• Energy optimisation
• Monitoring for water-loss and other
anomalous events
• Applications reach many departments, new
users, field crews, management…
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15. Taking the cake in SWN technology
Data fusion and analysis
Data management & display
Collection and communication
Sensing and control
Automatic systems analysePhysical layer
data from multiple sources, intelligently
monitoring for faults, controlling remote devices for optimal
operation, supporting decisions and network & business planning
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16. Which all boils down to…
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17. Hollywood ending?
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18. Are we smart yet?
Some challenges to cake bakers
• How accurate? How reliable? How effective?
• Do the systems work together, reusing data and
findings?
• Data quality – garbage in, garbage out
• Cost (cheaper data  more data  better results)
• Added value to consumers
(not just cost and availability)
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19. Smart Water Networks are only as
smart as the people who operate them
• “Smart Water Users”
• New work processes and skills!
• Co-evolution of technology and processes
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