6 facts about leakage which are not widely known, or just don’t get mentioned enough. At TaKaDu, we’ve been finding leaks and other network faults in customers’ data for several years now, so we have thousands and thousands of individual events to study, each conveniently recorded with the relevant sensor and operations data. This “gallery of leaks” is probably unique. Equally unique, is TaKaDu’s fully-automated statistical analysis of flow and pressure data. To develop and constantly refine this, we have had to study the finest details of a leak’s lifecycle and of the networks we monitor. Only through such study can we help analysts find leaks early, accurately, and reliably, despite the many factors which make this many times harder than theoretical or classroom examples. Look for my list of “How leaks hide in data”, as well as a cheap shot at typical water loss conference slides.
The upshot of all this is that we’ve been able to look at how leaks start, develop, and get repaired, and we noticed (amongst other observations) these 6 interesting and useful facts about “typical leaks”, all detailed and demonstrated in the slides.
3. Let’s revisit Active Leakage Control
Data
Flow
analysis Field
DMAs meters at Repairs
and surveys
inlets
targeting
3
4. Let’s revisit Active Leakage Control
Data
Data
Data Field
analysis
analysis
Continuous and
and Triggered by
monitoring targeting specific events
targeting
4
5. Let’s revisit Active Leakage Control
Data
Data
Data Field
Flow, GIS, calendar, analysis Early repairs
analysis
Continuous
network operations, and
and Triggered by
Less visible bursts
monitoring targeting specific events
pressure, weather, targeting Continuous service
schematics… Cost and capacity
5
6. You can’t just “look for flow increases”
Other Complex
Data
network utility
quality
events process
6
7. Analyst = Superman?
• Sifting
Check all data for all DMAs
• Statistical estimation
Is flow surprisingly high?
• Special knowledge
Was it caused by something else?
TaKaDu’s algorithms help boost this phase
7
11. Some leaks are easy to spot
• Great for marketing and conference slides
But…
• You don’t need a fancy algorithm to find this
• You may not even need an analyst
15. How leaks hide in data
• Small leaks (1 l/s) in large DMAs (XX l/s)
• Other flow increases (legitimate or other faults)
• Concurrent flow decrease
• Concurrent expected flow increase
• Random variability
Other Complex
• Wrong data! Data
network utility
quality
events process
• Gradual increase
15
16. Field experience and hard evidence
• Millions of sensor-days of data processed
• Thousands of leaks found, analysed and
reported at a dozen utilities
• Every utility is unique (really!)
• … But some basic truths are common
16
17. TOP FACTS ABOUT LEAKAGE
(WHICH DON’T GET ENOUGH PUBLICITY)
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18. Many leaks start abruptly at 0.5-5 l/s
4 l/s leak starts
• You can target significant leaks soon after start
• “DMA has 2 leaks, one X l/s and one Y l/s” vs.
“DMA total leakage is Z l/s”
18
19. Leaks start small and grow
• Early intervention is worth more than it seems
• A 1 l/s leak is just a 5 l/s leak waiting to happen
19
20. … Or they cause major visible bursts
• Early intervention prevents costly bursts
• You often have weeks and even months before
the “big pop”
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21. Do many leaks last years and years?
• Very rare in events noticeable in data (~ 1 l/s)
• Perhaps common in leaks of 0.1 l/s or 0.01 l/s?
Does anyone have solid evidence for this?
Are many DMAs leaking 20 x 0.1 l/s?
Tinier leaks are vast majority of repairs,
but do they matter?
• So where does (real) water loss come from?
21
22. Many leaks last weeks to months
(then they are repaired after a visible burst or ALC)
110 l/s burst
• Several megalitres lost before repair / big burst
• This is a huge component of water loss
22
23. Some leaks grow very slowly
• Rare, but possibly a distinct category of leak
• Anecdotally tougher to locate (many months)
• Maybe old pipe perforating at many locations?
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