146 research outputs found

    Automatic network response methodology for failure recovery or bursts in drinking water networks

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    This article presents a novel response methodology for the operational recovery of a drinking water network after an incident causes an interruption of service. The proposed optimization-based methodology allows computing the optimal set of interventions to be performed in order to mitigate, or even prevent, the impact of the incident on the network operation. Besides, a proof-of-concept scheme has been designed for the automatic generation of failure scenarios and the systematic implementation and validation of the proposed response methodology. Several results are presented to demonstrate the capability of the methodology to mitigate harmful incidents, as well as the performance improvements derived from the application of the obtained interventions.Peer ReviewedPostprint (author's final draft

    Reconfiguration of flow-based networks with back-up components using robust economic MPC

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    This paper addresses the post-fault selection of an actuators configuration for flow-based networks with back-up components. The proposed reconfiguration methodology consists of an offline and an online phase. On the one hand, an offline analysis looks for the minimal configurations for which the economic cost of the (best) steady-state trajectory that can be achieved using a robust model predictive control (MPC) policy is admissible. On the other hand, at fault detection time, an online search for the best actuators configuration to cope with the transient induced by the fault is conducted in the superset of each minimal configuration calculated offline. With this strategy, the final new configuration is computed by sequentially solving a set of mixed-integer programs whose constraints are derived from single-layer robust MPC schemes coupled with local controllers designed for the a priori minimal configurations identified offline. A portion of a water transport network is used to show the performance the proposed solution.Peer ReviewedPostprint (author's final draft

    Clustering-learning approach to the localization of leaks in water distribution networks

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    Leak detection and localization in water distribution networks (WDNs) is of great significance for water utilities. This paper proposes a leak localization method that requires hydraulic measurements and structural information of the network. It is composed by an image encoding procedure and a recursive clustering/learning approach. Image encoding is carried out using Gramian Angular Field (GAF) on pressure measurements to obtain images for the learning phase (for all possible leak scenarios). The recursive clustering/learning approach divides the considered region of the network into two sets of nodes using Graph Agglomerative Clustering (GAC), and trains a deep neural network (DNN) to discern the location of each leak between the two possible clusters, using each one of them as inputs to future iterations of the process. The achieved set of DNNs is hierarchically organized to generate a classification tree. Actual measurements from a leak event occurred in a real network are used to assess the approach, comparing its performance with another state-of-the-art technique, and demonstrating the capability of the method to regulate the area of localization depending on the depth of the route through the tree.The authors want to thank the Spanish national project “DEOCS (DPI2016-76493-C3-3-R)” project (which is finished nowadays) by its continuation: “L-BEST Project (PID2020-115905RB-C21) funded by MCIN/ AEI /10.13039/501100011033” and the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). Joaquim Blesa acknowledges the support from the Serra Húnter programPeer ReviewedPostprint (author's final draft

    Leak localization in water distribution networks using data-driven and model-based approaches

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    The detection and localization of leaks in water distribution networks (WDNs) is one of the major concerns of water utilities, due to the necessity of an efficient operation that satisfies the worldwide growing demand for water. There exists a wide range of methods, from equipment-based techniques that rely only on hardware devices to software-based methods that exploit models and algorithms as well. Model-based approaches provide an effective performance but rely on the availability of an hydraulic model of the WDN, while data-driven techniques only require measurements from the network operation but may produce less accurate results. This paper proposes two methodologies: a model-based approach that uses the hydraulic model of the network, as well as pressure and demand information; and a fully data-driven method based on graph interpolation and a new candidate selection criteria. Their complementary application was successfully applied to the Battle of the Leakage Detection and Isolation Methods (BattLeDIM) 2020 challenge, and the achieved results are presented in this paper to demonstrate the suitability of the methods.Peer ReviewedPostprint (author's final draft

    Model-free sensor placement for water distribution networks using genetic algorithms and clustering

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    This paper presents a model-free methodology for the placement of pressure sensors in water distribution networks (WDNs) with the aim of performing leak detection/localization tasks. The approach is based on a custom genetic algorithm (GA) optimization scheme, which considers a population whose individuals are binary vectors encoding the network nodes with/without sensors. The optimization process pursues the minimization of a distance-based metric, computed considering the pipe distance from the possible sensors to the complete set of nodes of the network, hence removing the necessity of a hydraulic model of the WDN. The methodology is completed by means of an iterative clustering technique that seeks the enhancement of incoming individuals. The proposed methodology is tested over a well-known case study, L-TOWN from the BattLeDIM2020 challenge, in order to assess its performance.Peer ReviewedPostprint (published version

    Leak detection in drinking water network using pressure-based classifier

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    Water leakage in drinking water networks (DWN) is the main reason for water loss in water networks. Considering the worldwide problem of water scarcity added to the challenges that a growing population brings, minimizing the water losses through detection of water leakages in DWN using efficient techniques is an urgent humanitarian need. This paper proposes a new data-driven leak detection method of defining classifiers based on limit pressure measurements in DWNs. This can get rid of complexities and application constraints of the model-based approach. The end result is an average 60% detection accuracy with limited data requirements. The proposed approach is applied to Hanoi DWN.Peer ReviewedPostprint (author's final draft

    A SWMM model for the Astlingen benchmark network

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    Real-time control of urban drainage systems is getting increasing attention due to its potential to reduce urban flooding and pollution to the receiving waters. Considering confidentiality requirements from the water companies, it is not easy for researchers or interested engineers to share models and data of real life urban drainage systems. However, it is very practical to use a benchmark to test and compare different methodologies. This paper contributes: (1) A hydrodynamic SWMM model of the Astlingen benchmark network, developed by working group ‘Integral RTC’ of the German Water Association, which enables a more widespread usage of the network due to SWMM being free and open source; (2) Applications of base case and equal-filling-degree rule-based control concepts to confirm usability of the SWMM model; (3) Preliminary result of model predictive control using this SWMM model.Peer ReviewedPostprint (published version

    A fully data-driven approach for leak localization in water distribution networks

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    © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents a data-driven technique for the localization of leaks in water distribution networks (WDN). The methodology requires hydraulic data, i.e., pressure measurements from a set of sensors installed throughout the network and topological information. Therefore, the hydraulic model of the WDN is not necessary for its operation. The hydraulic state of the complete set of nodes of the network is approximated by means of a graph-based interpolation technique. Then, a set of candidates where the leak can be located is achieved by comparing the computed states for both the leaky and nominal cases. The methodology is applied to a case study based on a real network, providing and discussing several graphical results and key performance indicators.The authors want to thank the RIS3CAT Utilities 4.0 SENIX project (COMRDI16-1-0055), as well as the Spanish national project DEOCS (DPI2016-76493-C3-3-R) and the Spanish State Research Agency through the María de Maeztu Seal of ExcelPeer ReviewedPostprint (author's final draft

    Challenge 4: Cyberphysical systems and internet of things

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    Accés lliure al text del llibre a la web de l'editorCyber-Physical Systems (CPS) and Internet of Things (IoT) are complementary paradigms in digitalization. Sensors and actuators, hardware designs and development platforms, architectures and computational frameworks, modeling, control and optimization, and potential applications are analyzed and presented from impact and main challenges up to strategic plan.Postprint (published version

    Chance-constrained stochastic MPC of Astlingen urban drainage benchmark network

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    In urban drainage systems (UDS), a proven method for reducing the combined sewer overflow (CSO) pollution is real-time control (RTC) based on model predictive control (MPC). MPC methodologies for RTC of UDSs in the literature rely on the computation of the optimal control strategies based on deterministic rain forecast. However, in reality, uncertainties exist in rainfall forecasts which affect severely accuracy of computing the optimal control strategies. Under this context, this work aims to focus on the uncertainty associated with the rainfall forecasting and its effects. One option is to use stochastic information about the rain events in the controller; in the case of using MPC methods, the class called stochastic MPC is available, including several approaches such as the chance-constrained MPC(CC-MPC) method. In this study, we apply CC-MPC to the UDS. Moreover, we also compare the operational behavior of both the classical MPC with perfect forecast and the CC-MPC based on different stochastic scenarios of the rain forecast. The application and comparison have been based on simulations using a SWMM model of the Astlingen urban drainage benchmark network. From the simulations, it was found that CSO volumes were larger when CC-MPC had overestimating forecast biases, while for MPC they increased with any presence of forecast biases.This document is the results of the research project funded by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656), internal project of TWINs, and also supported by Innovation Fond Denmark through the Water Smart City project (project 5157-00009B).Peer ReviewedPostprint (published version
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