One of the major contributors to water loss is underground leakage. Managing leaks using flow step-testing, network modeling, and field measurement

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1 c nservation Water distribution systems can experience large volumes of leakage that result in major financial, supply, and pressure losses. The authors describe a simple and efficient approach that combines flow step-testing and network modeling and solves the leakage detection problem using a direct application of hydraulic analysis and field-testing. The leak in a pipe segment is explicitly determined by analyzing the rate of change in the discrepancy between field-measured and modeled flow values. The method is well suited to bracket high leakage areas in the distribution system and is applicable to any pipe material. It was applied to an actual water distribution system, and results were validated by a leak detection survey using acoustic devices. The proposed method should prove useful to water utilities attempting to locate excessive pipe leaks in their distribution systems and thus conserve a precious natural resource while simultaneously reducing their energy consumption and carbon footprint. Managing leaks using flow step-testing, network modeling, and field measurement PAUL F. BOULOS AND ADEL S. ABOUJAOUDE One of the major contributors to water loss is underground leakage in water distribution systems. Other contributors to water loss include unauthorized use; unavoidable leaks; inaccurate master, industrial, commercial, and domestic meters; and unusual causes (AWWA, 2009; Leauber, 1997; Johnson, 1996). For some distribution systems, water loss may be in excess of 50% but typically ranges from 10 to 20% of production (AwwaRF, 1999; AWWA, 1987). Loss can be explicitly calculated by starting with total water produced, subtracting authorized uses, and then dividing the difference by total water produced. Leakage occurs in different components of the system, including transmission and distribution mains, service connection lines, valves, joints, and fire hydrants. It can originate from many sources, including deterioration of aging pipes and fittings, material defects, poor installation practices, changes in water pressure (water hammer) and demand pattern, high population density, heavy traffic volumes, movement of aboveground pipelines, aggressive soil conditions, and corrosion (AWWA, 1999). Large volumes of leakage can result in major financial, supply, and pressure losses as well as excessive energy consumption (pumping) and the associated carbon footprint. Excessive leakage can also cause contaminant intrusion 90 FEBRUARY 2011 JOURNAL AWWA 103:2 PEER-REVIEWED BOULOS & ABOUJAOUDE

2 under low- or negative-pressure conditions within the pipe, which can lead to detrimental or even fatal water quality episodes. The US Environmental Protection Agency estimates that water utilities in the United States will need to spend at least $6 billion per year over the next 20 years to rehabilitate failing water distribution pipes (Boulos et al, 2006; Lansey & Boulos, 2005). More recently, the American Society of Civil Engineers (ASCE) gave the nation s drinking water infrastructure a grade of D in its 2009 report card and estimated that leaking pipes result in a loss of 7 bgd of quality drinking water (ASCE, 2009). ASCE further concluded that US drinking water systems face an annual shortfall of at least $11 billion to replace aging facilities that are near the end of their useful life carbon footprint. and to comply with existing and future federal water regulations. This estimate does not include important provisions for improvements needed to meet growth in the demand for drinking water over the next 20 years. CURRENT STATE OF LEAK DETECTION AND MANAGEMENT Pressure reduction. Leakage normally varies exponentially with pressure and is reduced with a decrease in system pressure; therefore, managing leaks can be achieved by managing pressure in the distribution system while maintaining adequate minimum service levels (NRC, 2006). The most common methods of pressure reduction include zonal boundaries, pump and pressure control, fixed outlet control valves, flow-modulated control valves, and remote node control (Thornton et al, 2008). The efficacy of each method is normally assessed using hydraulic network modeling. Although these methods are generally effective at reducing the overall level of system leakage, they do not provide information about the location of the leaks (Al-Dowalia & Shammas, 1991). Benefits of leak reduction. Locating and repairing system leaks can significantly reduce the amount of water lost, the need to implement drastic conservation policies during droughts, the adverse risk of contamination, and water outages as well as lower costs for obtaining, treating, and pressurizing water supplies to meet current and future demands (AWWA, 2009). For water utilities, other benefits accruing to leak detection and repair include more efficient use of existing supplies; reduced carbon footprint; improved water conservation measures, environmental quality, and public relations; increased system operational efficiency, integrity, reliability, performance, and firefighting capability; Large volumes of leakage can result in major financial, supply, and pressure losses as well as excessive energy consumption (pumping) and the associated extended useful life of existing facilities and ability to increase service to new developments; improved knowledge of the distribution system; and delayed expansion of treatment plant capacity and construction of new sources of water supply. Water audit and leak detection programs. System audits and leak detection programs can help utilities reduce water losses (AWWA, 1999). Water audits involve detailed accounting of the distribution system inflows and outflows to provide an overall picture of distribution system efficiency and current water losses to determine the specific areas in the system experiencing excessive leakage. The overall goal is to identify, quantify, and verify water and revenue losses. These audits do not provide information about the precise location of the leaks themselves. Leak detection programs usually identify and prioritize the areas of high leakage using flow step-testing. The principal objective of flow step-testing is to continuously isolate portions of the distribution system where leakage is measured quantitatively (WHO, 2001). Locating leaks can also be carried out using acoustic equipment (leak detection survey). The exact locations of leaks can then be determined using leak noise correlators. Leak noise correlation. Acoustic loggers are normally installed on pipe fittings and are used to identify suspected leakage areas by listening for leak characteristics. By recording and analyzing the intensity and consistency of noise, the loggers are able to determine the likelihood of the presence of a leak. Noise is created by the leak as it escapes from the pressurized pipe. Similar to traditional sonic equipment, the correlator relies on the noise generated by a leak. The main difference, however, is in how the leak noise is detected. A correlator works by detecting the sound from the leak when it arrives at two sensors installed on pipe fittings or valve opening nuts on the pipe on either side of the suspected leak position. The location of the leak (X) is computed using a simple algebraic relationship (based on the principle of correlation) between the difference in the arrival time of the leak noise at each sensor ( t), which is measured from the crosscorrelation of the leak signals, the distance between sensors (L), and the propagation of sound waves in the pipe (V) as shown in Eq 1: X = [L (V t)]/2 (1) This approach is shown in Figure 1. Normally, acoustic equipment is effective for metal pipes because the leak signals transmit for relatively long distances; however, it BOULOS & ABOUJAOUDE PEER-REVIEWED 103:2 JOURNAL AWWA FEBRUARY

3 could be problematic for plastic piping because the signals transmit for only short distances. An assessment of the effectiveness of the various commonly used methods for locating leaks in plastic pipes was undertaken by the Water Research Foundation, and improvements for equipment and field procedures to increase their effectiveness were proposed (AwwaRF, 1999). In general, leak noise correlators are more efficient and more accurate than listening devices (Hunaidi, 2000). Leaks can also be located with nonacoustic technologies such as tracer gas, thermography, and ground-penetrating radar. However, use of these techniques is still limited, and their effectiveness has not been well established (Hunaidi, 2000). In recent years, many powerful numerical optimization models have been developed for estimating, locating, and minimizing leakage in water distribution systems, and these have been applied with varying levels of success (Nazif et al, 2010; Alvisi & Franchini, 2009; Magini et al, 2007; Yang et al, 2007; Araujo et al, 2006; Kapelan et al, 2004; Vitkovsky et al, 2000). The most successful of these applications use some form of genetic algorithms or artificial intelligence. However, field work and verification of these techniques with real-life systems, although not entirely absent, are for the most part still lacking (Colombo et al, 2009). Despite the availability of various optimization models, few are currently in practical use by water utilities. Water audit procedures and field-based leak detection methods continue to be regularly used to help control water loss (Hunaidi, 2000). In particular, flow step-testing is one of the most effective and practical field-based tools for identifying and quantifying leakage within specific areas (step areas) of the distribution system (WHO, 2001). FIGURE 1 Principle of correlation for leak detection Sensor Leak L distance between sensors, X location of the leak L The flow step-testing combined with network modeling demonstrated excellent conformity with the noise correlation technique. X Sensor 92 FEBRUARY 2011 JOURNAL AWWA 103:2 PEER-REVIEWED BOULOS & ABOUJAOUDE Flow step-testing. In flow step-testing, the step area is subdivided by the systematic closing of valves during the period of minimum nighttime flow, and the reduction in flow is recorded. Disproportionate drops in flow identify pipes with suspected leakage. The estimated leak flow corresponds to the difference between the observed drop in the measured flow versus the calculated drop in the modeled flow. The method is particularly well suited for identification of high leakage areas and for use on plastic pipes, where leak noise is absorbed and conventional acoustic methods are less effective. It also minimizes the use of detection required by sonic equipment and correlators (leak detection surveys). Having localized the leaks using flow step-testing, the correlators can be used to pinpoint the precise locations of the leaks along the pipes, and repairs can then be carried out. The sequence of step-testing, leak detection, and repair is continued until an acceptable volume of leakage in each step area is obtained. The main drawbacks of the traditional flow steptesting approach are the requirement of detailed maps of the distribution system, showing all of the water facilities (pipes, valves, pumps, tanks, and reservoirs), and the need to determine the proper sequences of valve closing to cut off different pipe sections in succession. In addition, the method requires consumption to be relatively stable, and its efficacy is usually limited by the lack of database support and user-friendly interfaces for data manipulation, hard-copy reporting (i.e., detailed field implementation manual), and graphical output display. These requirements suggest the need for an integrated network modeling approach to flow step-testing. A new method. The simple and efficient approach presented here overcomes these limitations by combining field flow step-testing with hydraulic network modeling. Today, many water utilities use hydraulic network models (e.g., EPANET) to plan improvements and optimize system operation (Boulos et al, 2006; NRC, 2006; AWWA, 2005; Rossman, 2000). The network model contains all pertinent water system facilities information and is used to delineate the step area, provide the necessary flow and consumption characteristics, and compute the optimal patterns of the systematic closing of valves. In addition, the new method is less sensitive to the structure of the network and the variation in demands. These capabilities provide a consistent modeling environment to assist water utilities in improving their effectiveness in locating areas with excessive leaks and in planning, designing, and implementing sound and cost-effective leakage management and system operational strategies. The approach was applied to an

4 actual water distribution system, and the results were validated with a leak detection survey using acoustic devices. METHODOLOGY The flow step-testing approach integrates network modeling and field testing to narrow leak locations to specific pipe segments of the water distribution system. Network modeling determines how to define and subsequently subdivide the step area through the systematic closing of valves and computes the total flow into the specific step area associated with each distinct valve closing operation. Field-testing involves taking flow readings while performing the required valve operations (see the illustration on page 90). At each step, the flow reading (at the flowmeter) is taken and compared with the modeled value, and the difference in flow rate is recorded. Any leakage isolated in a particular pipe segment sequence is shown as a drop and/or change in the difference in flow (flow discrepancy) between two successive steps. The flow discrepancy also represents the suspected leakage in the associated pipe segment sequence. The flow variation between two steps represents the water demand along the isolated pipe section. In the proposed method, an area with excessive leakage is bracketed into a tight zone (step area) with a flowmeter installed on the input main to each zone to record flow into the zone. The step area must be fed via only a single source (e.g., storage tank, pipe interconnection) and must not contain any loops (tree or branched network). It can be directly constructed using a breadth-first search or depth-first search spanning tree algorithm (Boulos et al, 2006). The step area thus becomes a treelike structure that is supplied through a single meter and isolated from the rest of the system by closing all boundary and circulation valves. This can be accomplished by (1) closing boundary valves to isolate the step area from the system (only one source of supply), (2) closing circulating valves to remove loops and create a tree network, and (3) placing a data logger on the flowmeter. Steps 1 and 3 can be avoided if the operation is applied over a district metered area (DMA), defined as an isolated area with a single metering point generally covering 500 to 3,000 properties. Figure 2 shows the schematic of a sample step area. Part A of the figure depicts the initial area, and part B shows the final treelike step area, obtained after closing boundary and circulating valves. An essential part of the process is to identify all valves that can be used during the test and all other valves that should be excluded from the test, such as boundary valves and inoperable (e.g., broken) valves. Identification of the latter group of valves is key in order to avoid mistakenly opening those valves during the test itself. The size of the zone is systematically reduced by working from the farthest valve from the meter and closing the FIGURE 2 Schematic of a sample flow step-test area Boundary valve Step valve Circulating valve Flowmeter Junction node A Initial step-test area B Final step-test area Tank/reservoir Tank/reservoir BOULOS & ABOUJAOUDE PEER-REVIEWED 103:2 JOURNAL AWWA FEBRUARY

5 valves to cut off different pipe sections in succession (so that less and less of the test area is supplied through the meter); at the same time, any changes in flow rate at the meter are recorded and compared with the modeled results. This sequence can be directly determined using the spanning-tree algorithm. Each valve must be shut long enough for workers to observe and measure the flow effect at the meter. The sequence of closing valves is followed until the flowmeter is reached (at which point the flow becomes zero). A significant discrepancy in flow rate between two successive steps indicates a leak in the section of pipe that was last shut off. The sequence is repeated by opening valves in reverse order. The opening and closing of valves should be performed slowly to avoid unwanted surges and breaks. To avoid hours of high demand such as in the morning, flow step-testing is normally carried out at night when the consumption is lowest and relatively unchanged (static); scheduling the step-testing at this time also helps to minimize supply interruption and inconvenience to customers. To alleviate potential water quality problems resulting from valve operation, some flushing may be required before flow step-testing is conducted in a targeted area of the distribution system (WHO, 2001). Pressure monitoring can also be carried out to validate the assumption that pressure remains relatively stable during the entire flow step-testing operation. Flow step-testing can be summarized as follows: Conduct the flow step-test at night during low and/ or slack demand. Define a flow step area with suspected high leakage. Close all boundary valves to establish a tight area. Close all circulating valves to remove loops, and create a tree network. Attach data logger to the flowmeter installed on the main supplying the flow step-test area. Start at the pipe and/or valve that is the farthest from the flowmeter. Close step valves in succession so less and less of the step area is supplied via the flowmeter, and record measured flow values. Follow the sequence of closing valves (as determined by the network model) back toward the meter until the reading is zero. Keep each step valve closed long enough to notice a reading effect at the meter. Reopen the step valves in reverse order. Depending on the topologic structure of the step area, more than one arrangement of step sequences may be possible. For example, in the sample step area shown in Figure 3, eight distinct valve sequences are feasible: V8, V7, V6, V5, V4, V3, V2, V1 V8, V7, V3, V2, V6, V5, V4, V1 V8, V7, V6, V5, V3, V2, V4, V1 V3, V2, V8, V7, V6, V5, V4, V1 V3, V2, V6, V5, V8, V7, V4, V1 V6, V5, V8, V7, V4, V3, V2, V1 V6, V5, V8, V7, V3, V2, V4, V1 V6, V5, V3, V2, V8, V7, V4, V1 The network model should be able to identify all possible step sequence arrangements and the desired sequence selected by field personnel based on optimal route. Care must be taken when conducting these tests to ensure that water quality and fire protection capabilities are not compromised. FLOW STEP-TEST EXAMPLE AND REAL-WORLD APPLICATION Illustrative example. An example is provided here to illustrate the calculation steps of the network modeling approach for leak detection. The flow step-test area of the example network is shown in Figure 4. The test area comprised six pipe sections and six demand nodes. Two leaks (of 1 flow unit each) were assumed for pipes P2 and P4, respectively. FIGURE 3 Example of feasible step sequences Step valve Junction node V3 V8 V7 V2 V1 V4 V5 V6 Flowmeter V valve 94 FEBRUARY 2011 JOURNAL AWWA 103:2 PEER-REVIEWED BOULOS & ABOUJAOUDE

6 Figure 5 provides flow results for each step sequence back toward the flowmeter. Moving from right to left (Figure 5), field crews sequentially shut off the valves. With each valve closure, the flow rate was reduced at the monitoring point on the left side of the figure. The valveclosing operations at valves V2 and V4 resulted in two distinct flow discrepancies, indicating the presence of a leak in pipes P2 and P4. Figure 6 shows flow results for the entire flow steptest. The symmetry along the x-axis (time axis) will be valid only when the demand loadings (and associated patterns) and operating conditions over the period of the flow step-test remain unchanged (static). However, the step discrepancies will not change with variations in the demand loading. This observation is key flow into the zone. for this method, because the redundancy of measurements provides a stable and efficient experimental design that can be reproduced under any demand conditions, provided the pressure does not change significantly. Flow calculations for the example network are summarized in Table 1. Application in an existing system. The efficacy of the method was tested on an actual water distribution system in the Gulf Cooperation Council (United Arab Emirates, Bahrain, Saudi Arabia, Oman, Qatar, Kuwait). The system serves a population of more than 1 million and comprises approximately 3,000 km of pipes (100 1,200 mm in diameter), several desalination plants and major pump stations, and approximately 100 DMAs. The identity of the corresponding water utility and the schematic of the distribution system are withheld for security reasons. Because of the rapid development of the project area, most of the water distribution pipes are less than In the proposed method, an area with excessive leakage is bracketed into a tight zone (step area) with a flowmeter installed on the input main to each zone to record 20 years old, with the majority installed in the past 10 years. A systemwide water balance (water audit) showed a relatively acceptable percentage of nonrevenue water (NRW) nearing 21%. Given the high production cost of desalinated water, however, economic studies recommended conducting a water loss reduction program to further lower the rate of water loss. A comprehensive leak detection program was carried out for more than 33 DMAs using flow step-testing combined with network modeling to narrow down the leakage areas to specific pipe sections in each DMA. The pipe sections identified with suspected leaks were successfully validated, and the exact positions of the leaks were then pinpointed by leak detection surveys using leak noise correlators. The flow step-testing combined with network modeling demonstrated excellent conformity with the noise correlation technique. This significantly sped up the leak detection management process and reduced associated costs because site operation for leakage reduction (leak detection survey) was narrowed down to a few pipe sections. As a result of the leakage management study and subsequent repairs, the NRW rate in each of the analyzed DMAs was reduced by 10 25%. CONCLUSION Today s water utilities can realize further benefits of hydraulic network modeling and field flow step-testing in managing leakage in their water distribution system infrastructures. The simple and efficient approach presented here combines field step-testing with network modeling to pinpoint pipes with suspected leaks for subsequent replacement or repair. The technique involves bracketing the test area with excessive leakage into a tight, branched network FIGURE 4 Example of flow step-test area Valve closed at step interval Closed boundary valve Water main External demand Junction node Leak: 1 unit 2 units 2 units Flowmeter 2 units 2 units P5 V4 Leak: 1 unit P4 2 units V3 P3 V2 P2 2 units V1 P1 V6 P6 V5 P pipe, V valve BOULOS & ABOUJAOUDE PEER-REVIEWED 103:2 JOURNAL AWWA FEBRUARY

7 FIGURE 5 Flow step-test results toward the meter for sample test area FIGURE 6 Complete flow step-test results for sample test area Measured flow Modeled flow V1 Disproportionate drop in flow when V2 and V4 are closed indicates suspected leaks in P2 and P V2 10 Flow unit 8 6 V3 Flow unit V4 4 2 V :00 12:10 12:20 12:30 12:40 12:50 1:00 1:10 Time a.m. P pipe, V valve V6 0 12:00 12:20 12:40 1:00 1:20 1:40 2:00 2:20 Time a.m. The value of each step represents the water demand along the pipe section. with a flowmeter installed on its input main. Working from the valve farthest from the flowmeter, the modeler systematically reduces the size of the area by closing the valves to cut off different pipes in succession, at the same time recording any changes in flow rate at the meter and comparing them with model results. A disproportionate change in flow discrepancy between two successive steps indicates a leak in the pipe that was last shut off. The method can effectively narrow down leaks to specific pipe segments of the system and is applicable to any pipe material. In order for the approach to be effective, the calibrated network model must include all mains, the asset data must be upto-date, and the diurnal demand profiles of key consumers and consumer types must be accurately reflected in the model. As recently noted by other researchers (Cheung et al, 2010), practicing engineers normally prefer using field experimentation and simple hydraulic network analysis rather than a complex mathematical optimization technique that is still an unattained reality for many water companies, especially in developing countries. The proposed approach can be further enhanced by linking to a geographic information system (GIS) to store, locate, manage, and display all pertinent water system facilities and produce comprehensive maps of the step areas. The data from the GIS environment are fed into the network model that produces the optimal flow step- TABLE 1 Flow step-test calculations for sample test area Step Time Valve Flow Flow Flow Step Sequence a.m. Operation Measured Modeled Difference Discrepancy Comment NA not applicable 0 12:10 NA 14 units 12 units 2 units NA 1 12:20 V1 12 units 10 units 2 units 0 units No leak 2 12:30 V2 9 units 8 units 1 units 1 units 1 unit leak 3 12:40 V3 7 units 6 units 1 units 0 units No leak 4 12:50 V4 4 units 4 units 0 units 1 units 1 unit leak 5 1:00 V5 2 units 2 units 0 units 0 units No leak 6 1:10 V6 0 units 0 units 0 units 0 units No leak 96 FEBRUARY 2011 JOURNAL AWWA 103:2 PEER-REVIEWED BOULOS & ABOUJAOUDE

8 testing sequences, which in turn can be evaluated by the GIS to provide utility personnel with a detailed field implementation manual for flow step-testing, detailing the proper sequences of valve operations. The resulting geospatial approach to flow step-test modeling will help to effectively communicate the schedule and/or progress information to field personnel, because they will be able to see in detail the temporal sequences of valve operations alongside the modeling results. ABOUT THE AUTHORS Paul F. Boulos (to whom correspondence should be addressed) is the president and chief operating officer of MWH Soft, 380 Interlocken Crescent, Ste. 200, Broomfield, CO 80021; paul.boulos@mwhsoft.com. He holds BS, MS, and PhD degrees in civil engineering from the University of Kentucky in Lexington and an MBA from Harvard University in Cambridge, Mass. Boulos has more than 24 years of experience in both the academic and corporate world, with extensive expertise in water resources engineering. He is the author of nine textbooks and more than 100 technical articles on water and wastewater engineering. Adel S. AbouJaoude is a manager and associate in the Environmental Engineering Department of Khatib & Alami in Beirut, Lebanon. Date of submission: 06/03/10 Date of acceptance: 08/19/10 JOURNAL AWWA welcomes comments and feedback at journal@awwa.org. REFERENCES Al-Dhowalia, K.H. & Shammas, N.K., Leak Detection and Quantification of Losses in a Water Network. Intl. Jour. Water Resources Development, 7:1:30. Alvisi, S. & Franchini, M., Multiobjective Optimization of Rehabilitation and Leakage Detection Scheduling in Water Distribution Systems. Jour. Water Resources Planning & Mngmnt. ASCE, 135:6:426. ASCE (American Society of Civil Engineers), Report Card for America s Infrastructure. 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Vitkovsky, J.P.; Simpson, A.R.; & Lambert, M., Leak Detection and Calibration Using Transients and Genetic Algorithms. Jour. Water Resources Planning & Mngmnt. ASCE, 126:4:262. WHO (World Health Organization), Leakage Management and Control A Best Practice Training Manual. WHO, Geneva. Yang, J.; Wen, Y.; & Li, P., Genetic Algorithm-enhanced Blind System Identification for Water Distribution Pipeline Leak Detection. Jour. Measurement Sci. & Technol., 18:7:2178. BOULOS & ABOUJAOUDE PEER-REVIEWED 103:2 JOURNAL AWWA FEBRUARY