Gerard Sanz

Leak Localization in Water Networks: A Model-Based Methodology Using Pressure Sensors Applied to a Real Network in Barcelona [Applications of Control]

The efficient distribution of water is a subject of major concern for water utilities and authorities [1]. While some leaks in water distribution networks (WDNs) are unavoidable, one of the main challenges in improving the efficiency of drinking water networks is to minimize leaks. Leaks can cause significant economic losses in fluid transportation and extra costs for the final consumer due to the waste of energy and chemicals in water treatment plants. Leaks may also damage infrastructure and cause third-party damage and health risks. In many WDNs, losses due to leakage are estimated to account up to 30% of the total amount of extracted water [2]; a very important issue in a world struggling to satisfy water demands of a growing population.

Sensitivity Analysis for Sampling Design and Demand Calibration in Water Distribution Networks Using the Singular Value Decomposition

AbstractResearch in water distribution networks during recent decades has often focused on calibration. There is no unique solution for this problem as the methodologies are developed depending on which parameters have to be calibrated and the final use of the model. This work presents a demand calibration methodology that identifies a set of patterns that minimize the error in predicted measurements. The singular value decomposition (SVD) of the sensitivity matrix is a powerful tool for solving the optimization problem. Additionally, in this work, the deep understanding of the SVD allows the selection of an alternative to the classic patterns. Each individual demand is defined as a combination of geographically distributed patterns. The membership of each demand to every pattern is produced naturally through the analysis of the SVD of the sensitivity matrix. Three types of memberships are considered: binary, positive, and free. The SVD analysis is also used to define the location of sensors for the calib...

A decision support system for on-line leakage localization

This paper describes a model-driven decision-support system (software tool) implementing a model-based methodology for on-line leakage detection and localization which is useful for a large class of water distribution networks. Since these methods present a certain degree of complexity which limits their use to experts, the proposed software tool focuses on the integration of a method emphasizing its use by water network managers as a decision support system. The proposed software tool integrates a model-based leakage localization methodology based on the use of on-line telemetry information, as well as a water network calibrated hydraulic model. The application of the resulting decision support software tool in a district metered area (DMA) of the Barcelona distribution network is provided and discussed. The obtained results show that the leakage detection and localization may be performed efficiently reducing the required time.

Leakage isolation in water distribution networks: A comparative study of two methodologies on a real case study

Leakages are present to some extent in all water-distribution systems. This paper compares two model based methodologies for leakage isolation in water distribution networks. Both are based on the pressure measurements and pressure sensitivity analysis of nodes in a network. Simulations of the network in presence and absence of leakage may provide an approximation of this sensitivity. The difference between both methodologies relies on how the information of the sensitivity matrix is handled. In the first approach, this information is binarised using a threshold. The resultant binary matrix is assumed as a signature matrix for leakages. One of the main issues in the binarisation process is the threshold selection. Even with the best selection of a threshold, binarisation implies loss of information. The second method is based on the use of the sensitivity matrix without any transformation in order to avoid loss of information. Results obtained on a real network (a District Metered Area (DMA) of Barcelona water distribution network) using both methods are compared. Finally, some discussions and conclusions about the limitations of both techniques and future work are presented.

Leakage localization in water networks using fuzzy logic

This paper presents a methodology for leakage localization using FIR (Fuzzy Inductive Reasoning). A real water network situated in Barcelona has been subdivided in zones which could contain a leakage. Two sensors measure pressures on two separated points of the network. A faulty fuzzy model for each zone and sensor is generated. Test data have been used for classification of leakages in order to evaluate how this methodology helps in leakage localization. Results are compared with another isolation methodology. All the work has been done using simulations carried out by EPANET connected with Matlab. FIR applications used are programmed in Matlab too.

Abnormal quality detection and isolation in water distribution networks using simulation models

This paper proposes a model based fault localisation method to deal with abnormal quality levels based on the chlorine measurements and chlorine sensitivity analysis in a water distribution network. A fault isolation algorithm which correlates on line the residuals, generated by comparing the available chlorine measurements with their estimation using a model, with the fault sensitivity matrix is used. The proposed methodology has been applied to a District Metered Area (DMA) in the Barcelona network.

Modelling and Simulation of Drinking-Water Networks

Water distribution network models are used by water companies in a wide range of applications. The availability of a good model allows the users to test any methodology that, otherwise, could not be applied directly to the real network. A model can be used to predict future states of the network, to analyse the effect of manipulating the real network before doing it, or to simulate faulty states to locate leakages, among others. This presents the basis of water distribution network modelling. The hydraulic equations are presented in their matrix form, which will be used in the following. Demands can be calculated, using the matrix model, if all heads or flows are known. However, this information is not available in a real case. Consequently, a hydraulic solver is needed to simulate the network, computing heads and flows from a predefined set of demands and boundary conditions. Results are obtained using the extended period simulations of the steady-state models. Transients are not considered due to their low importance in large networks.

Parameter Estimation: Definition and Sampling Design

Process control and supervision are based mainly on the use of models. These models have to be as accurate as possible to generate reliable results. Complex systems, like water distribution networks, need such models in order to comprehend them. Models presented in Chap. 3 are used in simulation, optimization, supervision, leak detection, etc. When the model is generated, large errors are introduced. These errors discourage the technicians unless they are corrected in a first calibration effort: macrocalibration. This is an ad hoc process that is done manually. The methodology, carried out by the experts, can be partially addressed using artificial intelligence (AI) algorithms. Once the major errors are solved, the parameter tuning, microcalibration, is posed as an optimization problem. Before these procedures are applied, the problem and the information available have to be analysed in order to assure the reliability of the resulting model. Given a number of parameters to be estimated, the measurements required for guaranteeing the identifiability and the well-posedness of the problem may be too exigent. Thus, the sampling design is often associated with a redefinition of the parameters to be estimated. In this chapter, both the parameterization and the sampling design are presented proposing a methodology that has given promising results with real water distribution networks.

Parameter Estimation: Online Calibration

Demands are unknown inputs that must be defined to solve the network’s hydraulic equations. They are not physical elements of the network, but are the driving force behind the hydraulic dynamics. A good calibration of demands is mandatory to obtain accurate results when simulating the hydraulic model. The simulation of the models, presented in previous chapters, is useful in a wide range of applications, as leakage detection and isolation, control, quality supervision and other issues presented in following chapters. This simulation is also useful, together with real measurements, for the estimation of demands. This chapter presents an overview of the existing calibration methods and a detailed description of an on-line calibration procedure. The demand calibration requires the parameterization and the sampling design. The three processes are carried out using the information provided by the SVD of the network sensitivity matrix. An academic network is used as case study for a better understanding of the whole methodology and a real application on a DMA is also presented. The methodology applied to demands can be adapted to other parameters in the network such as roughness and emitter coefficients.

Uncertainty effect on leak localisation in a DMA

The leak localisation methodologies based on data and models are affected by both uncertainties in the model and in the measurements. This uncertainty should be quantified so that its effect on the localisation methods performance can be estimated. In this paper, a model-based leak localisation methodology is applied to a real District Metered Area using synthetic data. In the generation process of the data, uncertainty in demands is taken into account. This uncertainty was estimated so that it can justify the uncertainty observed in the real measurements. The leak localisation methodology consists, first, in generating the set of possible measurements, obtained by Monte Carlo Simulation under a certain leak assumption and considering uncertainty, and second, in falsifying sets of nodes using the correlation with a leak residual model in order to signal a set of possible leaky nodes. The assessment is done by means of generating the confusion matrix with a Monte Carlo approach.