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Special Issue "Advances in the Monitoring, Diagnosis, and Optimisation of Water Systems"

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

Deadline for manuscript submissions: 10 May 2022.

Special Issue Editors

Dr. Miquel À. Cugueró-Escofet
E-Mail Website
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
E-Mail Website
Guest Editor
Insitut de Robòtica i Informàtica Industrial (IRI), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
Interests: real-time control of urban water networks (drinking water networks and urban water networks); leak detection and localization; quality monitoring; sensor data validation and reconstruction; water management
Special Issues, Collections and Topics in MDPI journals

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

Manuscript Submission Information

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 papers will be 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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 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 (4 papers)

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Research

Article
Robust Data-Driven Leak Localization in Water Distribution Networks Using Pressure Measurements and Topological Information
Sensors 2021, 21(22), 7551; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227551 - 13 Nov 2021
Viewed by 259
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 [...] Read more.
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
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Article
Estimation of Infiltration Volumes and Rates in Seasonally Water-Filled Topographic Depressions Based on Remote-Sensing Time Series
Sensors 2021, 21(21), 7403; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217403 - 07 Nov 2021
Viewed by 434
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 [...] Read more.
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
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Article
A Wireless Sensor Network Deployment for Soil Moisture Monitoring in Precision Agriculture
Sensors 2021, 21(21), 7243; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217243 - 30 Oct 2021
Viewed by 352
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 [...] Read more.
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
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Article
Transfer Learning in Wastewater Treatment Plant Control Design: From Conventional to Long Short-Term Memory-Based Controllers
Sensors 2021, 21(18), 6315; https://0-doi-org.brum.beds.ac.uk/10.3390/s21186315 - 21 Sep 2021
Viewed by 542
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 [...] Read more.
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
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Tentative Title:

Quality modelling and supervision in water distribution systems

Authors:

Ramon Pérez, Albert Martinez-Torrents, Manuel Martínez, Laura Vinardell, Xavier Martínez-Lladó, Irene Jubany

Abstract:

The quality of the drinking water distributed through the networks has become the main concern of most operators. The literature review presented in this work demonstrate the interest in modelling water quality and the different approaches proposed. Nevertheless, the real application of these models is far from utilising these models in the supervision of water distribution networks. This paper combines the use of validated models with the intensive study of a great amount of data and parameter estimation techniques. The models obtained focus on the most important variables for a Mediterranean country, chlorine and disinfection by-product (DBP). They are calibrated and validated with real data coming from the SCADA and analytics off-line.

 

Tentative Title:

Temporal Case-Based Reasoning applied to Intelligent Environmental Decision Support Systems

Authors:

Josep Pascual-Pañach, Miquel Sànchez-Marrè, Miquel Àngel Cugueró-Escofet

Abstract:

An important drawback when designing control and supervision systems for environmental processes is the need to provide ad-hoc solutions for each facility. In order to tackle this, the use of Artificial Intelligence (AI) techniques have been used in recent years in the design of Intelligent Decision Support Systems (IDSS). In previous works, a scalable Case-Based Reasoning (CBR) approach for the generation of control set-points has been proposed and tested in several real Waste Water Treatment Plants (WWTP), providing a general methodology also allowing scalability to further types of systems. However, the dynamic nature of environmental processes suggests temporal dependencies between cases. Thus, this paper presents a temporal CBR methodology and compares its performance with the former static CBR approach.

 

Tentative Title:

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

Authors:

Débora Alves, Joaquim Blesa, Eric Duviella and Lala Rajaoarisoa

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 the case of a leak-free scenario, these residuals can be used to detect pressure sensor faults. In a leak scenario, It is possible to determine the relative incidence of a leak in a node by using the network topology, 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 that is 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.

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