Posts filed under ‘Smart Water Grid’
Jim, a network analyst at a water utility, comes into work each morning and logs into multiple pieces of software. He has to be fluent in each of these systems and regularly switches between them in order to perform his activities. Jim creates new network jobs, updates current job statuses and commentary, while ensuring that the field teams, his management and the various other systems he uses are all kept up to date. Jim’s frustration with the inefficiency of the status quo is typical. Like many water utilities, the use of multiple systems increases the degree of human error, which leads to inefficiencies and potentially unsafe operations. The lack of system integration is one of the principal barriers to streamlining water utility operations and makes managing network events such as supply interruptions, asset failures and leaks even more complex.
Smart Water Networks in the U.S. – A Dream or Near-Future Reality? An Interview with Psomas President, Jacob Lipa
In June 2013, Psomas, a top-ranked consulting engineering firm formed a partnership with TaKaDu, a global leader in smart water network monitoring. Psomas now plans to serve the U.S. market by offering TaKaDu’s cloud based solution to monitor water distribution networks. In an interview with TaKaDu, Psomas President, Jacob Lipa offers his insights into the future of the U.S. water market and the important role of TaKaDu.
This is the fifth post in the “CTO Smart Water Insights” by Haggai Scolnicov, TaKaDu’s CTO.
I didn’t get to beautiful Utrecht to give this talk on SWANonomics at SWAN 2012, as I had intended, although I did get a great substitute to stand in for me.
Rather than talk about TaKaDu, network monitoring, or any of our technology, I wanted to use our few years’ experience in this dynamic part of the water industry to shine a light on some of the more surprising economic aspects of the Smart Water Networks revolution. Data revolutions are funny that way. It’s not just the tired truism that adoption of any technology is an economic process; new data has a way of changing what you know about the real world and your existing processes, and creates real new opportunities to take action differently.
Here are some of the unexpected headlines:
– Good news: smart monitoring drives spending increase on field crews
– Accepting new values discovered to entrenched KPIs goes through the exact 5 stages of grief
– Better leakage reduction means less Ml/day repaired
– The cost of water not lost is just a small part of the value of water loss reduction
This is the fourth post in the “CTO Smart Water Insights” by Haggai Scolnicov, TaKaDu’s CTO.
I went to Water Loss 2012 in Manila to tell water utilities one simple thing: data already collected under accepted “best practices” is all they need for a dramatic improvement to water loss control. Of course, that’s part of what you get when you deploy TaKaDu, but I wanted to focus on something else.
There are various tools and methodologies out there to use network monitoring data, and they are far from having been created equal. Whilst the data from sensors and other sources does hold the key to water loss control, and the principles as sketched on a conference slide are simple enough, real-world conditions make data analysis the trickiest link in the active leakage control chain. Three murky clouds – typically glossed over in water loss presentations – help to muddy the waters:
– Data quality (the meter values could be wrong)
– Other network events (the event you found may not be a leak)
– Complex utility process (you think there may be a small leak somewhere – what to do now?)
In this presentation (and the more detailed paper), I revisited the “traditional” Active Leakage Control process, highlighting the role of data analysis (manual and automated), revolving around leakage analyst’s work on the 3 “S”es of data analysis Supermen: Sifting, Statistical estimation, and Special knowledge. By boosting this difficult stage, utilities report they achieved significant quantifiable savings throughout the ALC process.
To do this, I listed some guidelines for coping with the 3 big uncertainties of data, network events, and the utility process, starting with detailed knowledge and understanding of these factors, to be addressed by suitable processing. As with many data analytic challenges, these real-world data “technicalities” are, in fact, the main challenge for data-driven ALC.
If you’re a pessimist, my slides are mostly a long list of impediments to active leakage control. If you’re an optimist, they are a collection of opportunities to do it better. If you work at TaKaDu, these slides are just what we have been doing for the past few years, what we’re good at doing, and what we need to keep doing. Have a look also at some of the other great talks and papers from the conference, too.
One of the nice things about being in the water utility business rather than in the electric or gas utility business is that water utilities don’t trace back their history only fifty or a hundred years. Some water utilities we’ve met trace back to the dawn of human urban settlement. That is certainly the case for Hagihon, Jerusalem’s water utility, named after the Gihon Springs. These springs were the main source of water for the inhabitants of ancient Jerusalem while also irrigating the adjacent Kidron valley and providing the city’s inhabitants with food.
To continue ensuring that Jerusalem can continue its thousands of years sustainable water use, Jerusalem’s Hagihon utility is one of the first utilities to have deployed a smart water network using TaKaDu’s water network monitoring solution.
This is the second post in a new series we’ll call “CTO Smart Insights” by Haggai Scolnicov, TaKaDu’s CTO.
To view the first post, go here.
When I was asked to speak at the Intelligent Cities Expo in Hamburg about Smart Water Networks, I assumed I would be on a double defensive: apologizing for an industry late to catch up on the data revolutions of the past hundred years, and struggling to demonstrate the value of data in water against a backdrop of data-poor, “conventional” engineering water innovation. I’m glad to say I was completely wrong!
At the water-themed session I was flanked by brilliant colleagues Steffen Schaefer from IBM and Francis Campan from Suez Environnement, talking data-driven solutions from start to finish. With these two industry giants helping to drive home the point (one from the data processing side, and one from the water business), the clear message was that smart water management is data-driven water management. We were practically “singing from the same hymn book”, perhaps not surprising, considering our three companies’ commitment to SWAN – Smart Water Networks Forum. Moreover, contrasting the session as impartially as I could with other sessions on power, heating, transportation, and other city infrastructures, water seemed to stand out as the industry with the clearest “program” (however unofficial) to get the right data and put it to good use.
In this presentation, I defined the “Smart Water Network Revolution” and the benefits it is bringing, talked about how policy-makers and water professionals can and should help it along, and did my best to dispel the four great myths about water utilities and adoption of data technology:
- Water utilities are too conservative or low-tech to adopt such technology
- Water is a low-value commodity, therefore not a magnet for industrial R&D innovation
- Water utilities are not yet ready for “smart data systems”
- It’s too big an investment
Flip through the slides for the reality behind these myths.
This is the first post in a new series we’ll call “CTO Insights” by Haggai Scolnicov, TaKaDu’s CTO.
At the recent Water Loss UK workshop and seminar – an interesting, edifying event, as always – I told some of the world’s leading water loss experts what I know about leaks. My talk was clearly “selling ice to Eskimos”, or (closer to the very pleasant Birmingham venue) “bringing coals to Newcastle”.
Really, I just wanted to tell everyone 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.
- Many leaks start abruptly at 0.5-5 l/s – so individual leaks can have significant impact on water loss, they do not “start at 0”, and there is an abrupt start visible in flow data.
- Leaks start small and grow – at least in many of the leaks we observed, so a 0.5 l/s leak is not “too small to bother”, it is just a 5 l/s leak waiting to happen; repairing a reasonably small leak shows (statistically) better active leakage control than repairing a large one!
- … Or they cause major visible bursts – Many visible bursts develop from a slow leak lasting weeks or months, so with good detection there is time to intervene and prevent the more expensive damage and repairs.
- Leaks rarely last for years – at least the noticeable ones from around 1 l/s and up, so perhaps “background leakage” is just a convenient myth? I asked my audience whether anyone had seen solid evidence for DMAs where “tiny leaks” accounted for any sizeable fraction of the estimated Non-Revenue Water figures, but it seems that background leakage is always assumed, never detected…
- Many leaks last weeks to months – after which they are repaired following a visible burst or through active leakage detection. This accounts for a huge part of the total water loss, and is almost always severely underestimated by water utilities, because of not realising how long the leaks run before an analyst or engineer becomes aware of them. Although a good leakage control program may keep the average flows supplied from increasing over the year, these long “bumps” in the graph can account for most of the utility’s water loss!
- Some (less common) leaks grow very slowly – increasing gradually over many months. Our customers have indicated that this may be a particularly hard to notice and hard to locate type of leak, perhaps physically different to others, and especially valuable when detected by TaKaDu.
I was half-expecting to be told off during the tea break for trying to teach the leakage experts about leaks rather than talking about software, which is what I am supposed to know about, not to mention contradicting or questioning some of the industry’s most cherished assumptions. Instead, I found a line of thoroughly intrigued practitioners and experts, excited by the potential of learning from a “gallery” of real leaks’ hard data. Some of them went so far as to say they had always doubted this or that “standard assumption” about leaks, but never found any decisive evidence until now. A few have since asked for some of this material, to help spread the word.
Here is what I had to say at the workshop, but I heartily recommend browsing the other presentations, available on the event website.