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© 2024 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 works, This article presents a leak localization methodology based on state estimation and learning. The first is handled by an interpolation scheme, whereas dictionary learning (DL) is considered for the second stage. The novel proposed interpolation technique exploits the physics of the interconnections between hydraulic heads of neighboring nodes in water distribution networks (WDNs). In addition, residuals are directly interpolated instead of hydraulic head values. The results of applying the proposed method to a well-known case study (Modena) demonstrated the improvements of the new interpolation method with respect to a state-of-the-art approach, both in terms of interpolation error (considering state and residual estimation) and posterior localization., Peer Reviewed, Postprint (author's final draft)

In this paper, leak detection and localization in water distribution networks will be reviewed. In particular, the paper presents the evolution of the methods from model-based towards data-based approaches, listing, describing and comparing the main and/or most recent methods of both categories. Besides, the practical applicability in real water utilities of different existing methods is discussed, outlining the advantages and limitations of model-based and data-driven methods for this task. A well-known case study is used to compare some of the more promising methods and illustrate their performances. Perspectives of the future evolution of the current existing methods are also provided., Peer Reviewed, Postprint (published version)

Water networks are crucial infrastructures for the sustainability of modern cities, and hence their proper operation is of great importance. This includes the fast detection, localization and repair of leaks, which may produce major water losses. This paper presents a comparison between two leak localization methods, which belong to opposite categories: model-based and data-driven. To this end, their main characteristics are reviewed, highlighting their differences and advantages/drawbacks, to finally display several results whose discussion allows to draw important conclusions for the future of the research field., Peer Reviewed, Postprint (published version)

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 Reviewed, Postprint (author's final draft)

In this paper, leak detection and localization in water distribution networks will be reviewed. In particular, the paper presents the evolution of the methods from model-based towards data-based approaches, listing, describing and comparing the main and/or most recent methods of both categories. Besides, the practical applicability in real water utilities of different existing methods is discussed, outlining the advantages and limitations of model-based and data-driven methods for this task. A well-known case study is used to compare some of the more promising methods and illustrate their performances. Perspectives of the future evolution of the current existing methods are also provided.

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.

Water networks are crucial infrastructures for the sustainability of modern cities, and hence their proper operation is of great importance. This includes the fast detection, localization and repair of leaks, which may produce major water losses. This paper presents a comparison between two leak localization methods, which belong to opposite categories: model-based and data-driven. To this end, their main characteristics are reviewed, highlighting their differences and advantages/drawbacks, to finally display several results whose discussion allows to draw important conclusions for the future of the research field.

In this paper, we present a nodal hydraulic head estimation methodology for water distribution networks (WDN) based on an Unscented Kalman Filter (UKF) scheme with application to leak localization. The UKF refines an initial estimation of the hydraulic state by considering the prediction model, as well as available pressure and demand measurements. To this end, it provides customized prediction and data assimilation steps. Additionally, the method is enhanced by dynamically updating the prediction function weight matrices. Performance testing on the Modena benchmark under realistic conditions demonstrates the method's effectiveness in enhancing state estimation and data-driven leak localization., Comment: This work has been submitted to IFAC for possible publication. It has 6 pages and 3 figures

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 Reviewed, Postprint (published version)

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 program, Peer Reviewed, Postprint (author's final draft)

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.

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.

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.

© 2022 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 works, In this paper, we propose a data-driven leak localization method for water distribution networks (WDNs) which combines two complementary approaches: graph-based interpolation and dictionary classification. The former estimates the complete WDN hydraulic state (i.e., hydraulic heads) from real measurements at certain nodes and the network graph. Then, we append to the actual measurements a subset of relevant estimated states to feed and train the dictionary learning scheme. Thus, the meshing of these two methods is explored, and several promising performance results are attained, even deriving different mechanisms to increase the resilience to classical issues (e.g., dimensionality, interpolation errors, etc.). The approach is validated using the L-TOWN benchmark proposed in the BattLeDIM2020 competition., Peer Reviewed, Postprint (author's final draft)

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 Reviewed, Postprint (author's final draft)

A control-oriented quality modeling approach is proposed for sewer networks, which can represent quality dynamics using simple equations in order to optimize pollution load from combined sewer overflows in large scale sewer network in real time. Total suspended solid has been selected as the quality indicator, regarding it is easy to be estimated through measuring turbidity and correlated with other quality indicators. The model equations are independent for different elements in sewer network, which allows a scalable usage. In order to ensure accuracy of the proposed models, a calibration procedure and a sensitivity analysis have been presented using data generated by virtual reality simulation. Afterwards, a quality-based model predictive control has been developed based on the proposed models. To validate effectiveness and efficiency of the modelling and optimization approaches, a pilot case, based on the Badalona sewer network in Spain is used. Application results under different scenarios show that the control-oriented modelling approach works properly to cope with quality dynamics in sewers. The quality-based optimization approach can provide strategies in reducing pollution loads in real time.

A control-oriented quality modeling approach is proposed for sewer networks, which can represent quality dynamics using simple equations in order to optimize pollution load from combined sewer overflows in large scale sewer network in real time. Total suspended solid has been selected as the quality indicator, regarding it is easy to be estimated through measuring turbidity and correlated with other quality indicators. The model equations are independent for different elements in sewer network, which allows a scalable usage. In order to ensure accuracy of the proposed models, a calibration procedure and a sensitivity analysis have been presented using data generated by virtual reality simulation. Afterwards, a quality-based model predictive control has been developed based on the proposed models. To validate effectiveness and efficiency of the modelling and optimization approaches, a pilot case, based on the Badalona sewer network in Spain is used. Application results under different scenarios show that the control-oriented modelling approach works properly to cope with quality dynamics in sewers. The quality-based optimization approach can provide strategies in reducing pollution loads in real time., Peer Reviewed, Postprint (author's final draft)

This 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.

This paper presents a complete methodology for the development of an integrated software architecture, which can achieve a closed-loop application between the integrated real-time control (RTC) and a virtual reality simulation for the urban drainage system (UDS). Quality measurements are considered during the simulation and optimization process. Model predictive control (MPC) and rule-based control (RBC) are the two main RTC methods embedded in this architecture. The proposed integration environment allows the different software components to efficiently and effectively communicate and work in a system-wide way, as well as to execute all the necessary steps regarding input parameter management, scenario configuration and results extraction. The proposed approaches are implemented into a pilot based on the Badalona UDS (Spain). Results from different scenarios with individual control approaches and rain episodes are evaluated and discussed.

© 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 works, This 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 Excel, Peer Reviewed, Postprint (author's final draft)

This paper presents a complete methodology for the development of an integrated software architecture, which can achieve a closed-loop application between the integrated real-time control (RTC) and a virtual reality simulation for the urban drainage system (UDS). Quality measurements are considered during the simulation and optimization process. Model predictive control (MPC) and rule-based control (RBC) are the two main RTC methods embedded in this architecture. The proposed integration environment allows the different software components to efficiently and effectively communicate and work in a system-wide way, as well as to execute all the necessary steps regarding input parameter management, scenario configuration and results extraction. The proposed approaches are implemented into a pilot based on the Badalona UDS (Spain). Results from different scenarios with individual control approaches and rain episodes are evaluated and discussed., Peer Reviewed, Postprint (published version)

An integrated pollution-based real-time control (RTC) approach is proposed for a sewer network (SN) integrated with wastewater treatment plants (WWTPs) in a sanitation system (SS) to mitigate the impacts of pollution from combined sewer overflows (CSOs) on ecosystems. To obtain the optimal solution for the SS while considering both quantity and quality dynamics for multiple objectives, model predictive control (MPC) is selected as the optimal control method. To integrate SN and WWTP management, a feedback coordination algorithm is developed. A closed-loop virtual-reality simulator is used to assess the results of the optimal management approach achieved by applying MPC. The Badalona SS (Spain) provides a pilot case study to assess the efficacy and applicability of the proposed approach. A comparison with local rule-based and volume-based control strategies currently in use indicates that the proposed integrated pollution-based RTC approach can reduce the pollutant loads released to the receiving environment.

© 2020. Elsevier, An integrated pollution-based real-time control (RTC) approach is proposed for a sewer network (SN) integrated with wastewater treatment plants (WWTPs) in a sanitation system (SS) to mitigate the impacts of pollution from combined sewer overflows (CSOs) on ecosystems. To obtain the optimal solution for the SS while considering both quantity and quality dynamics for multiple objectives, model predictive control (MPC) is selected as the optimal control method. To integrate SN and WWTP management, a feedback coordination algorithm is developed. A closed-loop virtual-reality simulator is used to assess the results of the optimal management approach achieved by applying MPC. The Badalona SS (Spain) provides a pilot case study to assess the efficacy and applicability of the proposed approach. A comparison with local rule-based and volume-based control strategies currently in use indicates that the proposed integrated pollution-based RTC approach can reduce the pollutant loads released to the receiving environment., Peer Reviewed, Postprint (author's final draft)

This paper presents a feedback simulation procedure for the real-time control (RTC) of urban drainage systems (UDS) with the aim of providing accurate state evolutions to the RTC optimizer as well as illustrating the optimization performance in a virtual reality. Model predictive control (MPC) has been implemented to generate optimal solutions for the multiple objectives of UDS using a simplified conceptual model. A high-fidelity simulator InfoWorks ICM is used to carry on the simulation based on a high level detailed model of a UDS. Communication between optimizer and simulator is realized in a feedback manner, from which both the state dynamics and the optimal solutions have been implemented through realistic demonstrations. In order to validate the proposed procedure, a real pilot based on Badalona UDS has been applied as the case study.

This paper presents a feedback simulation procedure for the real-time control (RTC) of urban drainage systems (UDS) with the aim of providing accurate state evolutions to the RTC optimizer as well as illustrating the optimization performance in a virtual reality. Model predictive control (MPC) has been implemented to generate optimal solutions for the multiple objectives of UDS using a simplified conceptual model. A high-fidelity simulator InfoWorks ICM is used to carry on the simulation based on a high level detailed model of a UDS. Communication between optimizer and simulator is realized in a feedback manner, from which both the state dynamics and the optimal solutions have been implemented through realistic demonstrations. In order to validate the proposed procedure, a real pilot based on Badalona UDS has been applied as the case study., Peer Reviewed, Postprint (author's final draft)

The main focus of the project consists in the design of the control of a fuel-cell energy management system, so that it can be validated in simulation to finally implement it employing a FPGA-based structure. The project will be centered in the control system design, in which different techniques can be considered (LMIs, sliding...) and the FPGA utilization, as the controller will be programmed directly on the programmable device using HDL programming tools.

Este proyecto trata numerosos aspectos relacionados con los conocimientos adquiridos en el grado, entre otros la electrónica analógica y digital, la simulación mediante el entorno de programación visual MATLAB® Simulink o la programación hardware de FPGA usando el lenguaje VHDL. Además, se han utilizado recursos completamente nuevos en cuanto a contacto previo, tales como la creación de mundos virtuales empleando el lenguaje de programación VRML, el empleo del hardware y software de ROBOTIS® (controladora, servos, cableado), etc. Todo esto en conjunto ha permitido simular los tres servos de un robot manipulador virtual, para comprobar y verificar el correcto funcionamiento de la controladora OpenCM9.04 de ROBOTIS., This project deals with several fields of engineering which students work on at my degree, as analogue and digital electronics, systems simulation employing graphical programming environments like MATLAB® Simulink or FPGA hardware programming using VHDL. Moreover, I have used completely new resources regarding previous contact, such as developing virtual worlds by VRML programming language or using ROBOTIS® hardware and software (controllers, servos, wiring), etc. All these aspects together have allow me to simulate three servos belonging to a virtual manipulator robot, in order to check the proper functioning of the ROBOTIS® OpenCM9.04 controller.