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Advances in the Real-Time Monitoring and Control of Urban Water...
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Advances in the Real-Time Monitoring and Control of Urban Water Networks

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Urban Water Management".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 22173

Special Issue Editor

Prof. Dr. Vicenç Puig

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Utilities in charge of the management of urban water networks are facing new challenges in their real-time operation because of limited water resources, intensive energy requirements, growing population, costly and aging infrastructure, increasingly stringent regulations, and increased attention toward the environmental impact of water use. Such challenges force network managers to improve the methods and techniques that they use for real-time monitoring and control.

The Guest Editor is seeking papers that present new approaches for the real-time monitoring and control of urban water networks based on advanced technologies of automation, computer science, and telecommunications to largely improve their efficiency in terms of water management, energy consumption, water loss minimization, and water quality guarantees. Papers illustrating the methods applied to real-life pilot demonstrations are highly encouraged to clear show the impact in regional networks (Spain).

Prof. Dr. Vicenç Puig
Guest Editor

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 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.

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. Water 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 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

  • 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

Published Papers (7 papers)

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Research

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21 pages, 8401 KiB  
Article
Development and Application of a Real-Time Flood Forecasting System (RTFlood System) in a Tropical Urban Area: A Case Study of Ramkhamhaeng Polder, Bangkok, Thailand
by Detchphol Chitwatkulsiri, Hitoshi Miyamoto, Kim Neil Irvine, Sitang Pilailar and Ho Huu Loc
Water 2022, 14(10), 1641; https://doi.org/10.3390/w14101641 - 20 May 2022
Cited by 13 | Viewed by 3959
Abstract
In urban areas of Thailand, and especially in Bangkok, recent flash floods have caused severe damage and prompted a renewed focus to manage their impacts. The development of a real-time warning system could provide timely information to initiate flood management protocols, thereby reducing [...] Read more.
In urban areas of Thailand, and especially in Bangkok, recent flash floods have caused severe damage and prompted a renewed focus to manage their impacts. The development of a real-time warning system could provide timely information to initiate flood management protocols, thereby reducing impacts. Therefore, we developed an innovative real-time flood forecasting system (RTFlood system) and applied it to the Ramkhamhaeng polder in Bangkok, which is particularly vulnerable to flash floods. The RTFlood system consists of three modules. The first module prepared rainfall input data for subsequent use by a hydraulic model. This module used radar rainfall data measured by the Bangkok Metropolitan Administration and developed forecasts using the TITAN (Thunderstorm Identification, Tracking, Analysis, and Nowcasting) rainfall model. The second module provided a real-time task management system that controlled all processes in the RTFlood system, i.e., input data preparation, hydraulic simulation timing, and post-processing of the output data for presentation. The third module provided a model simulation applying the input data from the first and second modules to simulate flash floods. It used a dynamic, conceptual model (PCSWMM, Personal Computer version of the Stormwater Management Model) to represent the drainage systems of the target urban area and predict the inundation areas. The RTFlood system was applied to the Ramkhamhaeng polder to evaluate the system’s accuracy for 116 recent flash floods. The result showed that 61.2% of the flash floods were successfully predicted with accuracy high enough for appropriate pre-warning. Moreover, it indicated that the RTFlood system alerted inundation potential 20 min earlier than separate flood modeling using radar and local rain stations individually. The earlier alert made it possible to decide on explicit flood controls, including pump and canal gate operations. Full article
(This article belongs to the Special Issue Advances in the Real-Time Monitoring and Control of Urban Water Networks)
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15 pages, 1297 KiB  
Article
Health-Aware Economic MPC for Operational Management of Flow-Based Networks Using Bayesian Networks
by Javier Pedrosa, Vicenç Puig and Fatiha Nejjari
Water 2022, 14(10), 1538; https://doi.org/10.3390/w14101538 - 11 May 2022
Cited by 2 | Viewed by 1224
Abstract
This paper presents a health-aware economic Model Predictive Control (EMPC) approach for the Prognostics and Health Management (PHM) of generalized flow-based networks. The proposed approach consists of the integration of the network reliability model obtained from a Bayesian network in the control model. [...] Read more.
This paper presents a health-aware economic Model Predictive Control (EMPC) approach for the Prognostics and Health Management (PHM) of generalized flow-based networks. The proposed approach consists of the integration of the network reliability model obtained from a Bayesian network in the control model. The controller is then able to optimally manage the supply taking into consideration the distribution of the control effort, to extend the life of the actuators by delaying the network reliability decay as much as possible. It also considers an optimal inventory replenishment policy based on a desired risk acceptability level, leading to the availability of safety stocks for unexpected excess demand in networks. The proposed implementation is illustrated with a real case study corresponding to an aggregate model of the Drinking Water transport Network (DWN) of Barcelona. Full article
(This article belongs to the Special Issue Advances in the Real-Time Monitoring and Control of Urban Water Networks)
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17 pages, 2245 KiB  
Article
Multi-Objective-Based Tuning of Economic Model Predictive Control of Drinking Water Transport Networks
by Carlos Ocampo-Martinez, Rodrigo Toro, Vicenç Puig, Jan Van Impe and Filip Logist
Water 2022, 14(8), 1222; https://doi.org/10.3390/w14081222 - 11 Apr 2022
Cited by 3 | Viewed by 1687
Abstract
In this paper, the tuning of economic model predictive control (EMPC) applied to drinking water transport networks (DWTNs) is addressed using multi-objective optimization approaches. The tuning strategies are based on Pareto front calculations of the underlying multi-objective problem. This feature represents an improvement [...] Read more.
In this paper, the tuning of economic model predictive control (EMPC) applied to drinking water transport networks (DWTNs) is addressed using multi-objective optimization approaches. The tuning strategies are based on Pareto front calculations of the underlying multi-objective problem. This feature represents an improvement with respect to the standard EMPC approach for weight tuning based on trial and error. Different multi-objective optimization methods with corresponding normalization approaches of the controller objectives are first studied to explore the dynamic nature of the Pareto fronts. An automated decision-making strategy is proposed to select the preferred controller parameters as a function of different disturbance values. The tuning requires an offline training phase and an online application phase. During the offline phase, the controller parameters are selected for different disturbances using the decision-making strategy. During the online phase, two approaches are evaluated: (i) exploiting the controller parameters with the highest frequency in the resulting histogram or (ii) using a regression model between the controller parameters and the disturbances. The proposed tuning strategies are applied to a real-life simulation case study based on the Barcelona DWTN. The simulation results show that the proposed tuning strategies outperform the baseline results by exploiting the periodicity of the water demands profile. Full article
(This article belongs to the Special Issue Advances in the Real-Time Monitoring and Control of Urban Water Networks)
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20 pages, 1806 KiB  
Article
A Leak Zone Location Approach in Water Distribution Networks Combining Data-Driven and Model-Based Methods
by Marlon Jesús Ares-Milián, Marcos Quiñones-Grueiro, Cristina Verde and Orestes Llanes-Santiago
Water 2021, 13(20), 2924; https://doi.org/10.3390/w13202924 - 18 Oct 2021
Cited by 12 | Viewed by 3889
Abstract
Model-based and data-driven methods are commonly used in leak location strategies in water distribution networks. This paper formulates a hybrid methodology in two stages that complements the advantages and disadvantages of data-driven and model-based strategies. In the first stage, a support vector machine [...] Read more.
Model-based and data-driven methods are commonly used in leak location strategies in water distribution networks. This paper formulates a hybrid methodology in two stages that complements the advantages and disadvantages of data-driven and model-based strategies. In the first stage, a support vector machine multiclass classifier is used to reduce the search space for the leak location task. In the second stage, leak location task is formulated as an inverse problem, and solved using a variation of the differential evolution algorithm called topological differential evolution. The robustness of the method is tested considering measurement and varying demand uncertainty conditions ranging from 5 to 15% of node nominal demands. The performance of the hybrid method is compared to the support vector machine classifier and topological differential evolution approaches as standalone methods of leak location. The hybrid proposal shows higher performance in terms of location accuracy, zone size, and computational load. Full article
(This article belongs to the Special Issue Advances in the Real-Time Monitoring and Control of Urban Water Networks)
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24 pages, 4489 KiB  
Article
Smart Water Infrastructures Laboratory: Reconfigurable Test-Beds for Research in Water Infrastructures Management
by Jorge Val Ledesma, Rafał Wisniewski and Carsten Skovmose Kallesøe
Water 2021, 13(13), 1875; https://doi.org/10.3390/w13131875 - 05 Jul 2021
Cited by 9 | Viewed by 3319
Abstract
The smart water infrastructures laboratory is a research facility at Aalborg University, Denmark. The laboratory enables experimental research in control and management of water infrastructures in a realistic environment. The laboratory is designed as a modular system that can be configured to adapt [...] Read more.
The smart water infrastructures laboratory is a research facility at Aalborg University, Denmark. The laboratory enables experimental research in control and management of water infrastructures in a realistic environment. The laboratory is designed as a modular system that can be configured to adapt the test-bed to the desired network. The water infrastructures recreated in this laboratory are district heating, drinking water supply, and waste water collection systems. This paper focuses on the first two types of infrastructure. In the scaled-down network the researchers can reproduce different scenarios that affect its management and validate new control strategies. This paper presents four study-cases where the laboratory is configured to represent specific water distribution and waste collection networks allowing the researcher to validate new management solutions in a safe environment. Thus, without the risk of affecting the consumers in a real network. The outcome of this research facilitates the sustainable deployment of new technology in real infrastructures. Full article
(This article belongs to the Special Issue Advances in the Real-Time Monitoring and Control of Urban Water Networks)
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18 pages, 2927 KiB  
Article
Water Quality-Based Double-Gates Control Strategy for Combined Sewer Overflows Pollution Control
by Zhongqing Wei, Haidong Shangguan, Jiajun Zhan, Ruisheng Lin, Xiangfeng Huang, Lijun Lu, Huifeng Li, Banghao Du and Gongduan Fan
Water 2021, 13(4), 529; https://doi.org/10.3390/w13040529 - 18 Feb 2021
Cited by 6 | Viewed by 2524
Abstract
The combined sewer overflows (CSO) pollution has caused many serious environmental problems, which has aroused a worldwide concern. Traditional interception-storage measures, which exhibit the disadvantages of the larger storage tank volume and the low concentration, cannot efficiently control the CSO pollution. To solve [...] Read more.
The combined sewer overflows (CSO) pollution has caused many serious environmental problems, which has aroused a worldwide concern. Traditional interception-storage measures, which exhibit the disadvantages of the larger storage tank volume and the low concentration, cannot efficiently control the CSO pollution. To solve this problem, a water quality-based double-gate control strategy based on the pollution based real-time control (PBRTC) rule was proposed, and the chemical oxygen demand (COD) concentration was taken as the control index. A case study was carried out in Fuzhou, China as an example, in which the hydraulic and water quality model were constructed to evaluate two schemes. According to the results, compared to the traditional scheme, the double-gate scheme can not only reduce the storage tank volume by 1515 m3, but also increase the average COD interception rate by 1.84 times, thus ensuring the effective and stable operation of the facility. Furthermore, the traditional scheme and the double-gate scheme were evaluated under design rainfall beyond the design return period, which confirmed the high performance of the double-gate scheme in controlling CSO pollution. Full article
(This article belongs to the Special Issue Advances in the Real-Time Monitoring and Control of Urban Water Networks)
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Review

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17 pages, 4361 KiB  
Review
Stormwater Runoff Treatment Using Rain Garden: Performance Monitoring and Development of Deep Learning-Based Water Quality Prediction Models
by Minsu Jeon, Heidi B. Guerra, Hyeseon Choi, Donghyun Kwon, Hayong Kim and Lee-Hyung Kim
Water 2021, 13(24), 3488; https://doi.org/10.3390/w13243488 - 08 Dec 2021
Cited by 10 | Viewed by 4107
Abstract
Twenty-three rainfall events were monitored to determine the characteristics of the stormwater runoff entering a rain garden facility and evaluate its performance in terms of pollutant removal and volume reduction. Data gathered during the five-year monitoring period were utilized to develop a deep [...] Read more.
Twenty-three rainfall events were monitored to determine the characteristics of the stormwater runoff entering a rain garden facility and evaluate its performance in terms of pollutant removal and volume reduction. Data gathered during the five-year monitoring period were utilized to develop a deep learning-based model that can predict the concentrations of Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), Total Nitrogen (TN), and Total Phosphorus (TP). Findings revealed that the rain garden was capable of effectively reducing solids, organics, nutrients, and heavy metals from stormwater runoff during the five-year period when hydrologic and climate conditions have changed. Volume reduction was also high but can decrease over time due to the accumulation of solids in the facility which reduced the infiltration capacity and increased ponding and overflows especially during heavy rainfalls. A preliminary development of a water quality prediction model based on long short-term memory (LSTM) architecture was also developed to be able to potentially reduce the labor and costs associated with on-site monitoring in the future. The LSTM model predicted pollutant concentrations that are close to the actual values with a mean square error of 0.36 during calibration and a less than 10% difference from the measured values during validation. The study showed the potential of using deep learning architecture for the prediction of stormwater quality parameters entering rain gardens. While this study is still in the preliminary stage, it can potentially be improved for use in performance monitoring, decision-making regarding maintenance, and design of similar technologies in the future. Full article
(This article belongs to the Special Issue Advances in the Real-Time Monitoring and Control of Urban Water Networks)
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