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This article presents the design of a control strategy for the Calais canal, a navigation canal located in a lowland area in northern France that is affected by tides. Moreover, the available actuators are discrete-valued and the hierarchy of operational objectives is time-varying. All these circumstances render water level regulation of the Calais canal a challenging problem. In view of this situation, the design of the overall control architecture is divided into a sequence of structured tasks, which are distributed among layers. The upper layer determines the current operating mode based on the analysis of several environmental and operational aspects. Information regarding the current mode is taken into account at the intermediate layer to select the appropriate optimization-based control problem, which is solved using lexicographic minimization. The optimal control setpoints are determined and sent to the lower layer, where scheduling problems are solved to select low-level control actions from a finite set to minimize the mismatch with respect to the optimal setpoints. Different realistic simulation scenarios are tested to demonstrate the effectiveness of the proposed approach., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Transport Engineering and Logistics

This paper presents the design of a model predictive control (MPC) for the Calais canal, located in the north of France for satisfactory management of the system. To estimate the unknown inputs/outputs arising from the uncontrolled pumps, a digital twin (DT) in the framework of a Matlab- is used to reproduce the dynamics of the canal, and the real database corresponding to a period of three days is employed to evaluate the control strategy. The canal is characterized by two operating modes due to high and low tides. As a consequence of this, time-varying constraints on the use of gates must be considered, which leads to the design of two multiobjective control problems, one for the high tide and another for the low tide. Furthermore, a moving horizon estimation (MHE) strategy is used to provide the MPC with unmeasured states. The simulation results show that the different objectives are met satisfactorily., 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)

© 2023 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 the design of a control strategy for the Calais canal, a navigation canal located in a lowland area in northern France that is affected by tides. Moreover, the available actuators are discrete-valued and the hierarchy of operational objectives is time-varying. All these circumstances render water level regulation of the Calais canal a challenging problem. In view of this situation, the design of the overall control architecture is divided into a sequence of structured tasks, which are distributed among layers. The upper layer determines the current operating mode based on the analysis of several environmental and operational aspects. Information regarding the current mode is taken into account at the intermediate layer to select the appropriate optimization-based control problem, which is solved using lexicographic minimization. The optimal control setpoints are determined and sent to the lower layer, where scheduling problems are solved to select low-level control actions from a finite set to minimize the mismatch with respect to the optimal setpoints. Different realistic simulation scenarios are tested to demonstrate the effectiveness of the proposed approach., Peer Reviewed, Postprint (author's final draft)

This paper introduces two methodologies to provide an optimum sensor deployment layout, one based on a model-based approach and the other entirely data-driven. The first method is formulated as an integer optimization problem, an optimization criterion consisting of minimizing the average topological distance. The second method is a new methodology to provide an optimum sensor placement regarding how many sensors to install without using hydraulic information but just exploiting the knowledge of the topology of the Water Distribution Networks. The method uses the Girvan-Newman clustering algorithm to ensure complete coverage of the network and the study of the installation of pressure sensors in the central nodes of each group, selected according to different metrics of topological centrality. The approach is illustrated in the Modena network.

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 paper presents a switched linear system representation of water canal dynamics to incorporate different operating modes, which arise due to the occurrence of extreme weather phenomena such as flooding and drought episodes. To guarantee the stability during mode switching, a proper analysis on permanence regions–given by a collection of equilibrium states–for the switched linear system is presented. The permanence region is computed within a compact set, which depends on an adequate level region for the canals. A suitable algorithm is used to formulate an asymptotic stable Model Predictive Control (MPC) that steers and maintains the states of the system inside the target region indefinitely in a feasible manner. This strategy is successfully tested in a simulation using a realistic model of a canal., Peer Reviewed, Postprint (author's final draft)

Model predictive control (MPC) has been widely employed to control a large variety of water systems, such as dams, irrigation canals, inland waterways, drinking water networks and wastewater treatment plants. Its predictive capabilities and the possibility to incorporate constraints make MPC well suited to address several, and sometimes opposite, management objectives linked to water systems. The design of MPC for water systems is usually performed via dedicated software (e.g., Matlab) and tested in simulation using dedicated hydraulic software. However, the implementation of MPC strategies in real systems requires additional development to allow for its embedding within the information systems that are used by system managers. A possible solution is to create a tool based on Python that can be easily integrated with the information systems of managers, and within which existing Matlab solutions can be incorporated. In this paper, the development a ready-to-use Python tool using a hierarchical MPC approach designed for the management of the Calais Canal is presented., This work has been supported by ARMINES through the contract nº1908V/1700661 and the IIW (Institution Intercommunale des Wateringues)., Peer Reviewed, Postprint (author's final draft)

This paper introduces two methodologies to provide an optimum sensor deployment layout, one based on a model-based approach and the other entirely data-driven. The first method is formulated as an integer optimization problem, an optimization criterion consisting of minimizing the average topological distance. The second method is a new methodology to provide an optimum sensor placement regarding how many sensors to install without using hydraulic information but just exploiting the knowledge of the topology of the Water Distribution Networks. The method uses the Girvan-Newman clustering algorithm to ensure complete coverage of the network and the study of the installation of pressure sensors in the central nodes of each group, selected according to different metrics of topological centrality. The approach is illustrated in the Modena network. © 2023 Institute of Physics Publishing. All rights reserved., Postprint (published version)

This work presents the design of a combined control and state estimation approach to simultaneously maintain optimal water levels and maximize hydroelectricity generation in inland waterways using gates and ON/OFF pumps. The latter objective can be achieved by installing turbines within canal locks, which harness the energy generated during lock filling and draining operations. Hence, the two objectives are antagonistic in nature, as energy generation maximization results from optimizing the number of lock operations, which in turn causes unbalanced upstream and downstream water levels. To overcome this problem, a two-layer control architecture is proposed. The upper layer receives external information regarding the current tidal period, and determines control actions that maintain optimal navigation conditions and maximize energy production using model predictive control (MPC) and moving horizon estimation (MHE). This information is provided to the lower layer, in which a scheduling problem is solved to determine the activation instants of the pumps that minimize the error with respect to the optimal pumping references. The strategy is applied to a realistic case study, using a section of the inland waterways in northern France, which allows to showcase its efficacy., Peer Reviewed, Postprint (author's final draft)

This work presents the design of a combined control and state estimation approach to simultaneously maintain optimal water levels and maximize hydroelectricity generation in inland waterways using gates and ON/OFF pumps. The latter objective can be achieved by installing turbines within canal locks, which harness the energy generated during lock filling and draining operations. Hence, the two objectives are antagonistic in nature, as energy generation maximization results from optimizing the number of lock operations, which in turn causes unbalanced upstream and downstream water levels. To overcome this problem, a two-layer control architecture is proposed. The upper layer receives external information regarding the current tidal period, and determines control actions that maintain optimal navigation conditions and maximize energy production using model predictive control (MPC) and moving horizon estimation (MHE). This information is provided to the lower layer, in which a scheduling problem is solved to determine the activation instants of the pumps that minimize the error with respect to the optimal pumping references. The strategy is applied to a realistic case study, using a section of the inland waterways in northern France, which allows to showcase its efficacy., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Transport Engineering and Logistics

This paper deals with the leak detection problem in Water Distribution Networks (WDN). A leak detection method based on the water demand analysis of District Metered Areas (DMAs) is proposed. Historical leak-free data of water demand flow is used to extract minimum, and maximum values, and statistical distributions of differences (errors) between demand flow and predicted values at the different time hours of the day. The concept of sensor fusion is applied to reduce measurement uncertainties. For this, a virtual measurement is generated that considers each hour of the day a feature and, combined, develops a more accurate error analysis capable of detecting leaks and estimating the leak size magnitude. Furthermore, to increase the accuracy of the leak detection method, prediction errors are analyzed in a moving time window. Finally, the performance of the proposed leak detection method is assessed by using actual data of different real DMAs of the Barcelona WDN., Peer Reviewed, Postprint (published version)

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.

This work presents the design of a combined control and state estimation approach to simultaneously maintain optimal water levels and maximize hydroelectricity generation in inland waterways using gates and ON/OFF pumps. The latter objective can be achieved by installing turbines within canal locks, which harness the energy generated during lock filling and draining operations. Hence, the two objectives are antagonistic in nature, as energy generation maximization results from optimizing the number of lock operations, which in turn causes unbalanced upstream and downstream water levels. To overcome this problem, a two-layer control architecture is proposed. The upper layer receives external information regarding the current tidal period, and determines control actions that maintain optimal navigation conditions and maximize energy production using model predictive control (MPC) and moving horizon estimation (MHE). This information is provided to the lower layer, in which a scheduling problem is solved to determine the activation instants of the pumps that minimize the error with respect to the optimal pumping references. The strategy is applied to a realistic case study, using a section of the inland waterways in northern France, which allows to showcase its efficacy.

Model predictive control (MPC) has been widely employed to control a large variety of water systems, such as dams, irrigation canals, inland waterways, drinking water networks and wastewater treatment plants. Its predictive capabilities and the possibility to incorporate constraints make MPC well suited to address several, and sometimes opposite, management objectives linked to water systems. The design of MPC for water systems is usually performed via dedicated software (e.g., Matlab) and tested in simulation using dedicated hydraulic software. However, the implementation of MPC strategies in real systems requires additional development to allow for its embedding within the information systems that are used by system managers. A possible solution is to create a tool based on Python that can be easily integrated with the information systems of managers, and within which existing Matlab solutions can be incorporated. In this paper, the development a ready-to-use Python tool using a hierarchical MPC approach designed for the management of the Calais Canal is presented., Transport Engineering and Logistics