Few businesses can operate for long while experiencing annual inventory shrinkage of 20 percent or more. Yet, that’s the situation faced by water utilities around the world as their distribution systems regularly pour treated, potable water down the proverbial drain. Water loss represents a major fraction of non-revenue water (NRW), and it's a problem that is easily addressed with the right tools.
According to the International Water Association’s best practice recommendations for water balance studies, more than 65 percent of NRW arises from unauthorized water consumption, meter inaccuracies, and leakage.
The Ontario Sewer and Watermain Construction Association in Canada reported that as much as $1 billion worth of fresh, clean drinking water disappears into the ground every year from rotting, leaky municipal water pipes.
Traditional Approaches to Detecting Water Leakage
Water audit studies focus on the calculation of supply balances for water systems or their subsystems such as district meter areas (DMAs). Over the last decade, the concepts and methods developed for system-wide water balance calculations have been based upon water assets’ physical data and the statistics of pipe bursts, service connections, and underground conditions. Performance measures and indicators are used to support the managerial approaches to minimize different components of water losses. These concepts and methods have been adopted by countries around the world.
There are several techniques to detect where leakage is occurring in a distribution system, including random or regular sounding surveys, step-testing of sub-systems, and the use of acoustic loggers as a survey tool among others.
Regular or random sounding survey can be conducted for leak detection. It uses a traditional listening stick, an electronic listening stick or a systematic ‘sweep’ using acoustic loggers. Leak noise correlators or ground microphones are then used to pinpoint the exact location of the leak. This technique is time consuming and not very efficient in terms of focusing on areas with potential leaks because the engineers or technicians often spend time looking for leaks in sections of the network where they do not exist.
Step-testing is conducted by making temporary successive valve closure to reduce the size of a system (or supply district), typically, one step-testing area contains about 500-1500 water service connections. The valves are closed for a short duration while simultaneously measuring pipe flow rate. The resultant reduction in flow rate following the closure of a particular valve indicates the total leakage plus legitimate night consumption in that section of the distribution system. If the resultant reduction is greater than anticipated, taking into account the number and type of customers in the section isolated, then it is an indication of a leak. Step tests are generally undertaken during the period of minimum night flow (often between 2:00 AM and 4:00 AM). Carrying out such a test at this time avoids causing supply problems to the majority of customers.
To avoid night work and shutting down of various parts of a distribution system, acoustic loggers can be used to detect leakage. The acoustic or noise loggers are installed on pipe fittings by way of a strong magnet and are programmed to listen for leak characteristics. Typically, noise is recorded at one second intervals over a period of two hours during the night, when background noise is likely to be lower. By recording and analyzing the intensity and consistency of noise, each logger indicates the likely presence (or absence) of a leak. Acoustic loggers can either be permanently located in the network or they can be deployed at certain points for a user definable period, often two nights.
In the United Kingdom, where on average more than 15 percent of water is lost, leakage targets are set as a key performance measure with severe financial penalties if targets are not met. To meet the requirement a number of tasks must be undertaken:
-- First, the level of water loss must be assessed.
-- Second, the location of the losses must be determined.
-- Third, reactive and proactive measures to improve the condition of the water mains must be adopted.
The problem is, existing leak detection processes (see sidebar, “Traditional Approaches to Detecting Water Leakage”) are time-consuming and costly. An interesting research effort at Bentley’s Haestad Methods Solution Center in Watertown, CT, is seeking to develop an innovative, cost-effective strategy for estimating the location and extent of hidden leakage. Included in the study are both distributed background leakage and high-intensity leakage “hot-spots.”
The leak detection approach proposed applies genetic algorithms (GA), a search technique based upon the principles of natural evolution and genetic reproduction. Using GA, leaks are simulated across the network and the results of each simulation are compared with the known flow metering and pressure monitoring locations. The working notion is that by making repeated runs with refinements introduced in a manner that emulates natural selection, an optimal solution can be found. This is an intriguing geospatial engineering modelling problem. Over the course of my 30 years in this industry, I have seen geospatial information systems converge tightly with the mainstream solutions for the modelling of municipal hydraulic network infrastructure.
Bentley spends a significant part of its R&D budget tightening integration among specialized industrial applications such as hydraulic systems modelling with GIS systems. At the Haestad Methods Solutions Center, we have been working for more than 25 years in modelling the hydraulics of buried water distribution mains. Our analysis engines are the perfect consumers of geospatial information systems that manage the mapping of water assets. If we could use our technologies to effectively map leakage zones and hot-spots, the industry would benefit, and we would have yet another new differentiator for our commercial solution.
Partnership Toward Innovation
Led by Dr. Zheng Wu, one of Bentley’s experts on advanced techniques for optimizing solutions to rather complex modelling problems, the study applies “fast-messy” GAs to optimally calibrate models and plan their operation. Dr. Wu had linked arms with Paul Sage, modelling development manager for United Utilities PLC in the United Kingdom, to jointly develop a technique for determining the most likely locations to concentrate exploration for underground leakage in systems.
Their goal was to discover a way to apply Bentley’s WaterGEMS technology to the problem. They had worked for several months, presented some preliminary findings at a couple of industry conferences, and were now ready to apply the technology on a large system – one for which a major leak had already been discovered. If this new technique could “find” the known leak, that would be a great proof-of-concept that might lead to wider adoption at the utility.
So, the two partners set up the simulations, letting the optimizing algorithms cook away, only to deliver a result that was a disappointing negative finding. The new approach failed to zero in on the known leak. This is the modelling equivalent of a missile test-launch failure. But how could it happen?
Our researchers examined the results and came up with a possible mechanism for the failure. The explanation lies in the fundamental working premise. Dr. Wu had formulated his strategy as a hydraulic model calibration problem. Utilities use hydraulic models to simulate operation and support decisions. This requires a calibration process in which the modeler adjusts and tunes modelling parameters, such as pipe friction roughness and estimated water demands, in order to coerce the model into matching actual observed pressures and flows.
The two investigators reasoned that leakage impacts performance in ways that should be approximated as pressure-dependent discharge locations. The calibration algorithm should then simultaneously estimate leakage locations and/or intensity while varying pipe roughness across the network.
The problem arose because the researchers challenged their new technique against a model that was in operational use at the utility. It had already been substantially calibrated! While leakage was accounted for in the course of calibration, it was distributed in a simplified way.
Upon inspection of the calibrated model, the modelers observed that some assigned pipe roughness values for pipes of similar age and material were inconsistent and varying spatially. They reasoned that these values might be artificially deflected as a side-effect of the calibration in the vicinity of major leaks.
Indeed, consideration of the hydraulics of a hypothetical leak determine that pipe values upstream of a leak can be expected to calibrate rougher than truth and those pipes downstream of a leak will calibrate smoother. The prior model calibration exercise had succeeded in obscuring an intense leak. After the researchers reset the roughness values to consistent and reasonable hydraulic handbook values and reran the optimization algorithm, the leakage calibration zeroed precisely into the known leakage hot-spot.
Success at last! Still, expectations must be managed. Coupling modelling and geospatial analysis might provide a sort of buried treasure map that operators can use as a basis for uncovering unallocated water losses. However, this technology will not deliver the analysis equivalent of X-ray vision that will show maintenance teams where to commence digging and repair. It does, though, show great promise in directing utility owners and operators to locations to focus testing and explore and test with their acoustic devices, saving time and minimizing cost.
Few businesses can operate for long while experiencing annual inventory shrinkage of 20 percent or more. Yet, that’s the situation faced by water utilities around the world as their distribution systems regularly pour treated, potable water down the proverbial drain. Water loss represents a major fraction of non-revenue water (NRW), and it's a problem that is easily addressed with the right tools.