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Advances in the Monitoring, Diagnosis, and Optimisation of Water Systems

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (10 May 2022) | Viewed by 36097

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Dr. Miquel À. Cugueró-Escofet

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Guest Editor

Advanced Control Systems Research Group, Polytechnic University of Catalonia (UPC-BarcelonaTech), Terrassa Campus, Gaia Research Bldg, Rambla Sant Nebridi, 22, 08222 Terrassa, Barcelona, Spain
Interests: fault diagnosis; system identification; intelligent decision support systems; process control; sensor placement; sensor data validation and reconstruction; system optimisation; data science; water systems; active noise control

Prof. Dr. Vicenç Puig

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Guest Editor

Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Barcelona, Spain
Interests: planning and control of autonomous systems; supervision and advanced control of processes and systems; control of large scale systems
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Special Issue Information

Dear Colleagues,

In the context of global climate change, with the increasing frequency and severity of extreme events—such as draughts and floods—which will likely provide growing uncertainty to water demand and jeopardise its availability, utilities in charge of the management of water systems face new operational challenges because of increasing resource scarcity, intensive energy requirements, growing populations—especially in urban areas—costly and ageing infrastructure, increasingly stringent regulations and rising attention towards the environmental impact of water use. The shift from a linear to a circular economy and the need for a transition to a low-carbon production system represents an opportunity to address these emerging challenges related to water, energy, and the inefficient use of resources. In this context, the increasing number of advanced installed sensors—and the corresponding increases in available data—allow for the implementation of so-called Industry 4.0 techniques, which are strongly focused on interconnectivity, automation, artificial intelligence and real-time data acquisition, and will facilitate the development of intelligent tools in order to tackle such challenges. These challenges impel network managers to improve the methods and techniques they use for the monitoring, diagnosis, prognosis, supervision, and optimisation of the performance of water-related systems, in order to catch up with the current sustainability agenda.

Guest Editors are seeking papers that present novel approaches for the monitoring, diagnosis, prognosis, supervision and optimisation of water systems based on state-of-the-art advanced technologies in different disciplines, for example, control, automation, computer science and telecommunications in the context of high system efficiency improvements in terms of water management, energy consumption, water loss, and water quality. Papers presenting methods applied to real pilots are highly encouraged in order to show real-life impacts, whilst narrowing the gap between theory and practice as regards transitioning to a circular economy framework.

Prof. Dr. Vicenç Puig
Dr. Miquel À. Cugueró-Escofet
Guest Editors

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

diagnosis
real-time monitoring
sensor data validation and reconstruction
prognosis
optimisation
intelligent decision support systems
data imputation
artificial intelligence
water systems

Published Papers (12 papers)

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Editorial

Jump to: Research, Review

3 pages, 198 KiB

Open AccessEditorial

Advances in the Monitoring, Diagnosis and Optimisation of Water Systems

by Miquel Àngel Cugueró-Escofet and Vicenç Puig

Sensors 2023, 23(6), 3256; https://doi.org/10.3390/s23063256 - 20 Mar 2023

Viewed by 880

Abstract

In the context of global climate change, with the increasing frequency and severity of extreme events—such as draughts and floods—which will likely make water demand more uncertain and jeopardise its availability, those in charge of water system management face new operational challenges because of increasing resource scarcity, intensive energy requirements, growing populations (especially in urban areas), costly and ageing infrastructures, increasingly stringent regulations, and rising attention towards the environmental impact of water use [...] Full article

(This article belongs to the Special Issue Advances in the Monitoring, Diagnosis, and Optimisation of Water Systems)

Research

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15 pages, 995 KiB

Open AccessArticle

Economic Linear Parameter Varying Model Predictive Control of the Aeration System of a Wastewater Treatment Plant

by Fatiha Nejjari, Boutrous Khoury, Vicenç Puig, Joseba Quevedo, Josep Pascual and Sergi de Campos

Sensors 2022, 22(16), 6008; https://doi.org/10.3390/s22166008 - 11 Aug 2022

Cited by 3 | Viewed by 1208

Abstract

This work proposes an economic model predictive control (EMPC) strategy in the linear parameter varying (LPV) framework for the control of dissolved oxygen concentrations in the aerated reactors of a wastewater treatment plant (WWTP). A reduced model of the complex nonlinear plant is represented in a quasi-linear parameter varying (qLPV) form to reduce computational burden, enabling the real-time operation. To facilitate the formulation of the time-varying parameters which are functions of system states, as well as for feedback control purposes, a moving horizon estimator (MHE) that uses the qLPV WWTP model is proposed. The control strategy is investigated and evaluated based on the ASM1 simulation benchmark for performance assessment. The obtained results applying the EMPC strategy for the control of the aeration system in the WWTP of Girona (Spain) show its effectiveness. Full article

(This article belongs to the Special Issue Advances in the Monitoring, Diagnosis, and Optimisation of Water Systems)

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14 pages, 3218 KiB

Open AccessArticle

Chlorine Concentration Modelling and Supervision in Water Distribution Systems

by Ramon Pérez, Albert Martínez-Torrents, Manuel Martínez, Sergi Grau, Laura Vinardell, Ricard Tomàs, Xavier Martínez-Lladó and Irene Jubany

Sensors 2022, 22(15), 5578; https://doi.org/10.3390/s22155578 - 26 Jul 2022

Cited by 3 | Viewed by 1566

Abstract

The quality of the drinking water distributed through the networks has become the main concern of most operators. This work focuses on one of the most important variables of the drinking water distribution networks (WDN) that use disinfection, chlorine. This powerful disinfectant must be dosed carefully in order to reduce disinfection byproducts (DBPs). The literature demonstrates researchers’ interest in modelling chlorine decay and using several different approaches. Nevertheless, the full-scale application of these models is far from being a reality in the supervision of water distribution networks. This paper combines the use of validated chlorine prediction models with an intensive study of a large amount of data and its influence on the model’s parameters. These parameters are estimated and validated using data coming from the Supervisory Control and Data Acquisition (SCADA) software, a full-scale water distribution system, and using off-line analytics. The result is a powerful methodology for calibrating a chlorine decay model on-line which coherently evolves over time along with the significant variables that influence it. Full article

(This article belongs to the Special Issue Advances in the Monitoring, Diagnosis, and Optimisation of Water Systems)

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20 pages, 1935 KiB

Open AccessArticle

Comparison of Optimisation Algorithms for Centralised Anaerobic Co-Digestion in a Real River Basin Case Study in Catalonia

by David Palma-Heredia, Marta Verdaguer, Vicenç Puig, Manuel Poch and Miquel Àngel Cugueró-Escofet

Sensors 2022, 22(5), 1857; https://doi.org/10.3390/s22051857 - 26 Feb 2022

Cited by 9 | Viewed by 1801

Abstract

Anaerobic digestion (AnD) is a process that allows the conversion of organic waste into a source of energy such as biogas, introducing sustainability and circular economy in waste treatment. AnD is an intricate process because of multiple parameters involved, and its complexity increases when the wastes are from different types of generators. In this case, a key point to achieve good performance is optimisation methods. Currently, many tools have been developed to optimise a single AnD plant. However, the study of a network of AnD plants and multiple waste generators, all in different locations, remains unexplored. This novel approach requires the use of optimisation methodologies with the capacity to deal with a highly complex combinatorial problem. This paper proposes and compares the use of three evolutionary algorithms: ant colony optimisation (ACO), genetic algorithm (GA) and particle swarm optimisation (PSO), which are especially suited for this type of application. The algorithms successfully solve the problem, using an objective function that includes terms related to quality and logistics. Their application to a real case study in Catalonia (Spain) shows their usefulness (ACO and GA to achieve maximum biogas production and PSO for safer operation conditions) for AnD facilities. Full article

(This article belongs to the Special Issue Advances in the Monitoring, Diagnosis, and Optimisation of Water Systems)

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12 pages, 359 KiB

Open AccessArticle

Pressure Sensor Placement for Leak Localization in Water Distribution Networks Using Information Theory

by Ildeberto Santos-Ruiz, Francisco-Ronay López-Estrada, Vicenç Puig, Guillermo Valencia-Palomo and Héctor-Ricardo Hernández

Sensors 2022, 22(2), 443; https://doi.org/10.3390/s22020443 - 07 Jan 2022

Cited by 11 | Viewed by 2696

Abstract

This paper presents a method for optimal pressure sensor placement in water distribution networks using information theory. The criterion for selecting the network nodes where to place the pressure sensors was that they provide the most useful information for locating leaks in the network. Considering that the node pressures measured by the sensors can be correlated (mutual information), a subset of sensor nodes in the network was chosen. The relevance of information was maximized, and information redundancy was minimized simultaneously. The selection of the nodes where to place the sensors was performed on datasets of pressure changes caused by multiple leak scenarios, which were synthetically generated by simulation using the EPANET software application. In order to select the optimal subset of nodes, the candidate nodes were ranked using a heuristic algorithm with quadratic computational cost, which made it time-efficient compared to other sensor placement algorithms. The sensor placement algorithm was implemented in MATLAB and tested on the Hanoi network. It was verified by exhaustive analysis that the selected nodes were the best combination to place the sensors and detect leaks. Full article

(This article belongs to the Special Issue Advances in the Monitoring, Diagnosis, and Optimisation of Water Systems)

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24 pages, 4568 KiB

Open AccessArticle

Water Quality Indicator Interval Prediction in Wastewater Treatment Process Based on the Improved BES-LSSVM Algorithm

by Meng Zhou, Yinyue Zhang, Jing Wang, Yuntao Shi and Vicenç Puig

Sensors 2022, 22(2), 422; https://doi.org/10.3390/s22020422 - 06 Jan 2022

Cited by 14 | Viewed by 2519

Abstract

This paper proposes a novel interval prediction method for effluent water quality indicators (including biochemical oxygen demand (BOD) and ammonia nitrogen (NH3-N)), which are key performance indices in the water quality monitoring and control of a wastewater treatment plant. Firstly, the effluent data regarding BOD/NH3-N and their necessary auxiliary variables are collected. After some basic data pre-processing techniques, the key indicators with high correlation degrees of BOD and NH3-N are analyzed and selected based on a gray correlation analysis algorithm. Next, an improved IBES-LSSVM algorithm is designed to predict the BOD/NH3-N effluent data of a wastewater treatment plant. This algorithm relies on an improved bald eagle search (IBES) optimization algorithm that is used to find the optimal parameters of least squares support vector machine (LSSVM). Then, an interval estimation method is used to analyze the uncertainty of the optimized LSSVM model. Finally, the experimental results demonstrate that the proposed approach can obtain high prediction accuracy, with reduced computational time and an easy calculation process, in predicting effluent water quality parameters compared with other existing algorithms. Full article

(This article belongs to the Special Issue Advances in the Monitoring, Diagnosis, and Optimisation of Water Systems)

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16 pages, 861 KiB

Open AccessArticle

Two Simultaneous Leak Diagnosis in Pipelines Based on Input–Output Numerical Differentiation

by Adrián Navarro-Díaz, Jorge-Alejandro Delgado-Aguiñaga, Ofelia Begovich and Gildas Besançon

Sensors 2021, 21(23), 8035; https://doi.org/10.3390/s21238035 - 01 Dec 2021

Cited by 4 | Viewed by 1615

Abstract

This paper addresses the two simultaneous leak diagnosis problem in pipelines based on a state vector reconstruction as a strategy to improve water shortages in large cities by only considering the availability of the flow rate and pressure head measurements at both ends of the pipeline. The proposed algorithm considers the parameters of both leaks as new state variables with constant dynamics, which results in an extended state representation. By applying a suitable persistent input, an invertible mapping in x can be obtained as a function of the input and output, including their time derivatives of the third-order. The state vector can then be reconstructed by means of an algebraic-like observer through the computation of time derivatives using a Numerical Differentiation with Annihilatorsconsidering its inherent noise rejection properties. Experimental results showed that leak parameters were reconstructed with accuracy using a test bed plant built at Cinvestav Guadalajara. Full article

(This article belongs to the Special Issue Advances in the Monitoring, Diagnosis, and Optimisation of Water Systems)

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19 pages, 640 KiB

Open AccessArticle

Robust Data-Driven Leak Localization in Water Distribution Networks Using Pressure Measurements and Topological Information

by Débora Alves, Joaquim Blesa, Eric Duviella and Lala Rajaoarisoa

Sensors 2021, 21(22), 7551; https://doi.org/10.3390/s21227551 - 13 Nov 2021

Cited by 12 | Viewed by 2191

Abstract

This article presents a new data-driven method for locating leaks in water distribution networks (WDNs). It is triggered after a leak has been detected in the WDN. The proposed approach is based on the use of inlet pressure and flow measurements, other pressure measurements available at some selected inner nodes of the WDN, and the topological information of the network. A reduced-order model structure is used to calculate non-leak pressure estimations at sensed inner nodes. Residuals are generated using the comparison between these estimations and leak pressure measurements. In a leak scenario, it is possible to determine the relative incidence of a leak in a node by using the network topology and what it means to correlate the probable leaking nodes with the available residual information. Topological information and residual information can be integrated into a likelihood index used to determine the most probable leak node in the WDN at a given instant k or, through applying the Bayes’ rule, in a time horizon. The likelihood index is based on a new incidence factor that considers the most probable path of water from reservoirs to pressure sensors and potential leak nodes. In addition, a pressure sensor validation method based on pressure residuals that allows the detection of sensor faults is proposed. Full article

(This article belongs to the Special Issue Advances in the Monitoring, Diagnosis, and Optimisation of Water Systems)

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18 pages, 3077 KiB

Open AccessArticle

Estimation of Infiltration Volumes and Rates in Seasonally Water-Filled Topographic Depressions Based on Remote-Sensing Time Series

by Pavel P Fil, Alla Yu Yurova, Alexey Dobrokhotov and Daniil Kozlov

Sensors 2021, 21(21), 7403; https://doi.org/10.3390/s21217403 - 07 Nov 2021

Cited by 1 | Viewed by 2272

Abstract

In semi-arid ecoregions of temperate zones, focused snowmelt water infiltration in topographic depressions is a key, but imperfectly understood, groundwater recharge mechanism. Routine monitoring is precluded by the abundance of depressions. We have used remote-sensing data to construct mass balances and estimate volumes of temporary ponds in the Tambov area of Russia. First, small water bodies were automatically recognized in each of a time series of high-resolution Planet Labs images taken in April and May 2021 by object-oriented supervised classification. A training set of water pixels defined in one of the latest images using a small unmanned aerial vehicle enabled high-confidence predictions of water pixels in the earlier images (Cohen’s Κ = 0.99). A digital elevation model was used to estimate the ponds’ water volumes, which decreased with time following a negative exponential equation. The power of the exponent did not systematically depend on the pond size. With adjustment for estimates of daily Penman evaporation, function-based interpolation of the water bodies’ areas and volumes allowed calculation of daily infiltration into the depression beds. The infiltration was maximal (5–40 mm/day) at onset of spring and decreased with time during the study period. Use of the spatially variable infiltration rates improved steady-state shallow groundwater simulations. Full article

(This article belongs to the Special Issue Advances in the Monitoring, Diagnosis, and Optimisation of Water Systems)

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24 pages, 11975 KiB

Open AccessArticle

A Wireless Sensor Network Deployment for Soil Moisture Monitoring in Precision Agriculture

by Jaime Lloret, Sandra Sendra, Laura Garcia and Jose M. Jimenez

Sensors 2021, 21(21), 7243; https://doi.org/10.3390/s21217243 - 30 Oct 2021

Cited by 38 | Viewed by 7766

Abstract

The use of precision agriculture is becoming more and more necessary to provide food for the world’s growing population, as well as to reduce environmental impact and enhance the usage of limited natural resources. One of the main drawbacks that hinder the use of precision agriculture is the cost of technological immersion in the sector. For farmers, it is necessary to provide low-cost and robust systems as well as reliability. Toward this end, this paper presents a wireless sensor network of low-cost sensor nodes for soil moisture that can help farmers optimize the irrigation processes in precision agriculture. Each wireless node is composed of four soil moisture sensors that are able to measure the moisture at different depths. Each sensor is composed of two coils wound onto a plastic pipe. The sensor operation is based on mutual induction between coils that allow monitoring the percentage of water content in the soil. Several prototypes with different features have been tested. The prototype that has offered better results has a winding ratio of 1:2 with 15 and 30 spires working at 93 kHz. We also have developed a specific communication protocol to improve the performance of the whole system. Finally, the wireless network was tested, in a real, cultivated plot of citrus trees, in terms of coverage and received signal strength indicator (RSSI) to check losses due to vegetation. Full article

(This article belongs to the Special Issue Advances in the Monitoring, Diagnosis, and Optimisation of Water Systems)

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26 pages, 1351 KiB

Open AccessArticle

Transfer Learning in Wastewater Treatment Plant Control Design: From Conventional to Long Short-Term Memory-Based Controllers

by Ivan Pisa, Antoni Morell, Ramón Vilanova and Jose Lopez Vicario

Sensors 2021, 21(18), 6315; https://doi.org/10.3390/s21186315 - 21 Sep 2021

Cited by 12 | Viewed by 2338

Abstract

In the last decade, industrial environments have been experiencing a change in their control processes. It is more frequent that control strategies adopt Artificial Neural Networks (ANNs) to support control operations, or even as the main control structure. Thus, control structures can be directly obtained from input and output measurements without requiring a huge knowledge of the processes under control. However, ANNs have to be designed, implemented, and trained, which can become complex and time-demanding processes. This can be alleviated by means of Transfer Learning (TL) methodologies, where the knowledge obtained from a unique ANN is transferred to the remaining nets reducing the ANN design time. From the control viewpoint, the first ANN can be easily obtained and then transferred to the remaining control loops. In this manuscript, the application of TL methodologies to design and implement the control loops of a Wastewater Treatment Plant (WWTP) is analysed. Results show that the adoption of this TL-based methodology allows the development of new control loops without requiring a huge knowledge of the processes under control. Besides, a wide improvement in terms of the control performance with respect to conventional control structures is also obtained. For instance, results have shown that less oscillations in the tracking of desired set-points are produced by achieving improvements in the Integrated Absolute Error and Integrated Square Error which go from 40.17% to 94.29% and from 34.27% to 99.71%, respectively. Full article

(This article belongs to the Special Issue Advances in the Monitoring, Diagnosis, and Optimisation of Water Systems)

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Review

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48 pages, 3881 KiB

Open AccessReview

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

by Liping Yang, Joshua Driscol, Sarigai Sarigai, Qiusheng Wu, Christopher D. Lippitt and Melinda Morgan

Sensors 2022, 22(6), 2416; https://doi.org/10.3390/s22062416 - 21 Mar 2022

Cited by 24 | Viewed by 7452

Abstract

Water features (e.g., water quantity and water quality) are one of the most important environmental factors essential to improving climate-change resilience. Remote sensing (RS) technologies empowered by artificial intelligence (AI) have become one of the most demanded strategies to automating water information extraction and thus intelligent monitoring. In this article, we provide a systematic review of the literature that incorporates artificial intelligence and computer vision methods in the water resources sector with a focus on intelligent water body extraction and water quality detection and monitoring through remote sensing. Based on this review, the main challenges of leveraging AI and RS for intelligent water information extraction are discussed, and research priorities are identified. An interactive web application designed to allow readers to intuitively and dynamically review the relevant literature was also developed. Full article

(This article belongs to the Special Issue Advances in the Monitoring, Diagnosis, and Optimisation of Water Systems)

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