Experiences Using Water Network Analysis Modeling for Leak Localization

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Maude Gaines
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1 Experiences Using Water Network Analysis Modeling for Leak Localization S. Sethaputra*, S. Limanond*, Z. Y. Wu**, P. Thungkanapak***, K. Areekul*** * Technology Service and Consulting 1656 Co., Ltd. Bangkok 10700, Thailand ** Bentley Systems, Incorporated, Watertown, CT 06795, USA *** Metropolitan Waterworks Authority of Thailand, Bangkok 10210,Thailand Abstract Leak localization using traditional step testing involves extensive resource and equipment deployment, and creates water supply service interruptions during the step testing period. Recently, leak localization using Water Network Analysis (WNA) modeling software has been proposed as an alternative, so as to circumvent the aforementioned deployment issues of steptesting. Firstly, the water distribution system is partitioned into a DMA and a hydraulic model is constructed for the DMA. In addition to metering the inflow into the DMA, pressure loggers are also deployed into the field within the study DMA to collect the field pressures along with hydraulic boundary conditions. The field observed data are then imported into a state-of-the-art leakage detection optimization model to predict the most likely leakage hotspots. Hundreds of thousands of possible leakage locations are evaluated and the most likely leakage hotspots are found such that the model-simulated flows and pressures match as closely as possible with the field observed values. This paper reports initial experiences in using WNA modeling software for leak localization for a DMA in the Metropolitan Bangkok area. The model-predicted leakage hotspots are compared with actual step testing results. The results show that WNA modeling is a promising alternative method for leak localization. The accuracy of leak localization using the WNA software depends on the following factors: (i) Quality of model calibration (ii) Quality of input data Quantifications of these effects are the subject for further investigations. Keywords: Leak Localization, Water Network Analysis 1

traditional step testing involves extensive resource and equipment deployment, and creates water supply service interruptions during the step testing period.

2 Leak Localization Leak localization is a process to identify and prioritize pipe sections with comparatively high leakages. Conventionally, it is implemented by a field process known as a step test. As the test involves closing of valves, continuous supply of water to consumers is interrupted and hence is carried out during the night hours to minimize service interruption. In modern practice of water leakage reduction, the observation of disproportionately high minimum night flow (MNF) initiates the execution of a step test, the result of which indicates pipe sections that may contain leaks. Sounding, an acoustic method of leak detection, is subsequently performed to pinpoint leak locations for excavation and repair. As step testing requires careful planning and is expensive to deploy, this paper provides a first attempt to assess the potential of water network analysis (WNA) software for leak localization. Water Network Analysis is an attempt to simulate flow in pipe networks based on mass balance and energy conservation laws. Its required inputs include piping configuration of the service area (how are the pipes connected in the area), and water consumption (both in space and time). The physical laws instantiated with data inputs are generally referred to as a Mathematical Model. Steps required for hydraulic modeling are: (i) model development, (ii) model calibration, (iii) model verification, and (iv) model application. In this paper, the model 1 is created using off-shelf software that is user-friendly for user to undertake all the modeling tasks. A unique feature of the model used in this paper is its ability of leakage hotspots optimization (Wu and Sage 2007) that accounts for pressure-dependent flow or leakage based on the FAVAD principle 2. The model automatically performs all of the four steps mentioned, resulting in outputs as a prioritized list of likely high leakage locations. Description of Selected DMA The selected service area is an established DMA called DMA in Prachachun Area of the Metropolitan Bangkok. It has comparable high average pressure at 10 meter, suitable for subsequent application of acoustic sounding techniques such as noise correlator, has a number of properties around 2,000 households, and has one single inlet that is convenient for step testing. The DMA information relevant to water network analysis, such as DMA properties, including map showing pipe connections, locations of DMA inlet, and mobile pressure sensors, time series of Pressure and Flow Data, and the results of Step Testing indicating pipe sections with disproportional high leakages, are displayed in Figures 1 4, respectively. 1 WaterGEMS: a proprietary commercial software 2 FAVAD=Fixed and Variable Area Discharge 2

In modern practice of water leakage reduction, the observation of disproportionately high minimum night flow (MNF) initiates the execution of a step test, the result of which indicates pipe sections

3 Figure 1: Map of DMA in Prachachun Area with Locations of DMA Inlet and Mobile Pressure Sensors Figure 2: Observed Pressure Time Series Data for DMA

4 Figure 3: Observed Flow Time Series Data for DMA

5 Pipes that indicated high leakage from step testing Figure 4: Results of Step Test - Pipe Sections With High Leakages Shown Model Calibration After constructing the baseline model with pipe configurations and other intrinsic input parameters, the model is ready for calibration. Calibration makes use of the observed flow and pressure data (item 2 of the former section) to evaluate parameter adjustment by comparing the observed and the simulated values. It is conventionally carried out by trial-and-error approach that is usually time consuming and leads to a limited understanding of the system characteristics. The model calibration method used in this paper does the calibration automatically using a Genetic Algorithm 3 to adjust the parameter values such that differences between predicted and observed flows (including leakages) are minimal. In addition, it allows engineers to predict the likely leakage hotspots as well as undertake model calibration tasks. Outputs of the leakage hotspots optimization are the list of prioritized nodes that connect with highly leaky pipes. In this paper, the identified leakage hotspots are compared with results from step testing. Results from the leakage calibration are shown in Figures 5 8. Figures 5 and 6 show that Root Mean Square Errors are low, indicating that the data for the simulation and real world data matches each other closely. Figure 7 shows the fitness of various calculated solutions, and indicates that Solution 1 is the best calculated solution for this simulation. Figure 8 shows the leakage nodes that were identified with the Darwin Calibrator simulation. Within Darwin 3 See Bentley,

Calibration makes use of the observed flow and pressure data (item 2 of the former section) to evaluate parameter adjustment by comparing the observed and the simulated values.

6 Calibrator, if the Adjusted Emitter Coefficients 4 are greater than zero, this indicates a potential leakage location. As can be seen, 14 different leakage nodes were calculated through the simulation. Figure 5: Hydraulic Grade Data (RMSE= 0.54 m) 4 See Bentley,

As can be seen, 14 different leakage nodes were calculated through

7 Figure 6: Flow Data (RMSE = 0.34 l/s) Figure 7: Fitness Values (Solution 1 = 2.376, Solution 2 = 2.444, Solution 3 = 2.449) 7

8 Figure 8: Number of Leakage Nodes = 14 Comparison of Results Figure 9 shows results of leak localization obtained from the two sources: step testing and Water Network Analysis. Susceptible high-leak pipe sections obtained from step testing are indicated by arrows, whereas leakage hotspots predicted by WaterGEMS Darwin Calibrator are indicated by large red dots and numbered from 1 to 14. Table 1 demonstrates leakage hotspots with corresponding Emitter Coefficients. As can be seen from Figure 9, Darwin Calibrator did predict leakage nodes at or near the pipe sections indicated to have high possibility for leaks by step testing. There were also leakages predicted at several other locations throughout the DMA. Ideally these sections would be subsequently step tested again to confirm or deny the presence of leaks. Unfortunately due to time restrictions these follow-up step tests were not carried out. 8

from 1 to 14. Table 1 demonstrates leakage hotspots with corresponding Emitter Coefficients.

9 Node Adjusted Emitter Coefficient J J J J J J J J J J J J J J Table 1 shows Leakage Hotspots as computed by the WNA software and corresponding Emitter Coefficients Pipes that indicated high leakage from step testing Figure 9: Comparison of Step Test Leakage Points with Leakages Predicted in the WNA Model 9

040 Table 1 shows Leakage Hotspots as computed by the WNA software and corresponding Emitter Coefficients

10 Conclusions Comparison of results in Figure 9 suggests the potential of the WNA software for leak localization in a water distribution network. Ideally, with additional time and resources, further step tests and comparisons would be carried out to verify whether the model accurately predicted leaks throughout the system, and the extent to any errors that may exist. However, in this first step of experimentation of using a WNA model for leakage prediction in the Metropolitan Bangkok, it can be seen that WNA modeling is a promising method to detect leakages in a DMA. The accuracy of the simulation and leak predictions will depend largely on the quality of model calibration (both the algorithm used in the software and the input data). Factors, such as user s experience in using the calibration tool, user s understanding in the system hydraulics and number of pressure measurement points, demand profile, and node resolution need further investigation on how they affects model accuracy. The case study shows that the software works well, and is stable and flexible. With the input data of adequate quality, the leakage prediction tool is able to facilitate the process of leakage detection process. References T. Chuenchom, S. Limanond, U. Makmaitree, S. Tavorntaveevong, J. Pingclasai, R.S. Mckenzie, Experiences With Enterprise Water Loss Management System Deployment At Bangkok s Metropolitan Waterworks Authority, 2009 Bentley Systems, Water leakage detection and reduction with WaterCAD & WaterGEMS V8 XM s Darwin Calibrator, 2008 Wu, Z. Y. and Sage P. (2007) Pressure Dependent Demand optimization for Leakage Detection in Water Distribution Systems Water Management Challenges in Global Change: CCWI2007 and SUWM2007 Conference, Sept. 3-5, 2007, De Montfort University, Leicester, UK, pp

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