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Processes
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Optimization and Control of Integrated Water Systems
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Optimization and Control of Integrated Water Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (10 November 2021) | Viewed by 11736

Special Issue Editors

Prof. Dr. Pastora Isabel Vega Cruz

E-Mail Website
Guest Editor
Department of Computer Science and Automatics, Universidad de Salamanca, Salamanca, Spain
Interests: optimal operation and control of wastewater treatment systems; distributed control of sewer systems; optimization of integrated water systems (IWS); multi-agent-based MPC distributed control; model predictive control and economic model predictive control of IWS; integrated design; advanced control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ramón Vilanova Arbós

E-Mail Website
Guest Editor
Department of Telecommunication and System Engineering, Universitat Autonoma de Barcelona, Barcelona, Spain
Interests: wastewater control systems; PID control systems; event-based control; systems with uncertainty; analysis of control systems with several degrees of freedom; application to environmental systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the current market-driven industrial field, complex plants that deal with material recycling and heat integration are increasingly appearing, motivated by the considerable improvement of economic efficiency. Beyond economic motivations, the transition to a more sustainable production model implies increasing system complexity, introducing more recycling to save energy and raw materials and minimize emissions to water, air, and soil. On the other hand, in the pursuit of more sustainable scenarios, the circular economy concept develops strategies and ideas for the treatment processing and reuse of waste, providing a second life, reducing the final waste to a minimum, as well as generating economic opportunities.

Particularly, these issues apply to water systems as they are clear candidates for improving water quality, safety, and reliability minimizing energy consumption and gas emissions. Although traditionally, the wastewater treatment plant (WWTP), the sewer system, and the receiving water bodies have been considered as separate systems, they are interacting parts of a more complex system, the integrated water system (IWS). Therefore, this Special Issue will explore the more appealing control issues that appear when performing coordinated control of the IWS. Moreover, advanced control and supervision methodologies can be used within a circular economy framework to offer solutions to account for the interactions between the subsystems comprising the IWS. These techniques can be applied by using actual communication and computation facilities at reasonable costs. The circular economy concept allows for the assessment of environmental impact from a life cycle perspective and for the consideration of many relevant aspects into the IWS analysis.

This Special Issue on the "Optimization and Control of Integrated Water Systems" will bring together methodologies for the optimization, supervision, and control of integrated water systems using advanced operational strategies. Plant-wide supervision and control schemes, based on game and artificial intelligence theories, are appealing approaches for an efficient solution to this complex problem, as long as they are properly integrated with classical ones. Furthermore, their implementation within a circular economy framework will ensure better behavior of the integrated water system as a whole.

Topics include but are not limited to the following:

  • Theoretical and practical advances in modeling, simulation, and control of integrated water systems
  • Hybrid systems and mixed-logical dynamical modeling for control of IWS
  • Decentralized and agent-based modeling of IWS
  • Optimization of IWS and their components
  • Environmental and economic distributed MPC control of IWS
  • Multi-agent game-based distributed MPC of IWS
  • Decentralized, cooperative, and coordinated distributed MPC control of IWS
  • Networked systems and sectorization methodologies
  • Weather forecasting disturbance inclusion in control algorithms applied to integrated water systems
  • Learning Strategies for multi-agent systems
  • Data-driven fault detection, diagnosis, and prognosis solutions for integrated water systems
  • Life cycle assessment of integrated water systems
  • Water–energy nexus
  • Smart technology and IoT impact on integrated water system management

This special issue is focused more on processes. Papers focus on automation and control may choose our joint Special Issue in Automation (ISSN 2673-4052).

Prof. Pastora Isabel Vega Cruz
Prof. Ramón Vilanova Arbós
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 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. Processes is an international peer-reviewed open access monthly 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 2400 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

  • Distributed predictive control (DMPC)
  • fuzzy logic, multi-agent systems (MAS)
  • reinforced learning systems
  • game theory
  • data-driven fault detection, diagnosis and prognosis methods
  • deep learning
  • integrated water systems (IWS)

Published Papers (5 papers)

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Research

50 pages, 7206 KiB  
Article
Practical Computational Approach for the Stability Analysis of Fuzzy Model-Based Predictive Control of Substrate and Biomass in Activated Sludge Processes
by Pedro M. Vallejo LLamas and Pastora Vega
Processes 2021, 9(3), 531; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9030531 - 17 Mar 2021
Cited by 1 | Viewed by 1521
Abstract
This paper presents a procedure for the closed-loop stability analysis of a certain variant of the strategy called Fuzzy Model-Based Predictive Control (FMBPC), with a model of the Takagi-Sugeno type, applied to the wastewater treatment process known as the Activated Sludge Process (ASP), [...] Read more.
This paper presents a procedure for the closed-loop stability analysis of a certain variant of the strategy called Fuzzy Model-Based Predictive Control (FMBPC), with a model of the Takagi-Sugeno type, applied to the wastewater treatment process known as the Activated Sludge Process (ASP), with the aim of simultaneously controlling the substrate concentration in the effluent (one of the main variables that should be limited according to environmental legislations) and the biomass concentration in the reactor. This case study was chosen both for its environmental relevance and for special process characteristics that are of great interest in the field of nonlinear control, such as strong nonlinearity, multivariable nature, and its complex dynamics, a consequence of its biological nature. The stability analysis, both of fuzzy systems (FS) and the very diverse existing strategies of nonlinear predictive control (NLMPC), is in general a mathematically laborious task and difficult to generalize, especially for processes with complex dynamics. To try to minimize these difficulties, in this article, the focus was placed on the mathematical simplification of the problem, both with regard to the mathematical model of the process and the stability analysis procedures. Regarding the mathematical model, a state-space model of discrete linear time-varying (DLTV), equivalent to the starting fuzzy model (previously identified), was chosen as the base model. Furthermore, in a later step, the DLTV model was approximated to a local model of type discrete linear time-invariant (DLTI). As regards the stability analysis itself, a computational method was developed that greatly simplified this difficult task (in a local environment of an operating point), compared to other existing methods in the literature. The use of the proposed method provides useful conclusions for the closed-loop stability analysis of the considered FMBPC strategy, applied to an ASP process; at the same time, the possibility that the method may be useful in a more general way, for similar fuzzy and predictive strategies, and for other complex processes, was observed. Full article
(This article belongs to the Special Issue Optimization and Control of Integrated Water Systems)
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18 pages, 791 KiB  
Article
Modular Feedback Control of Networked Systems by Clustering: A Drinking Water Network Case Study
by José María Maestre, Francisco Lopez-Rodriguez, Francisco Javier Muros and Carlos Ocampo-Martinez
Processes 2021, 9(2), 389; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9020389 - 20 Feb 2021
Cited by 7 | Viewed by 2070
Abstract
This article presents a method based on linear matrix inequalities (LMIs) for designing a modular feedback control law, whose synthesis guarantees the system stability, while switching to different network topologies. Such stability is achieved by means of a common Lyapunov function to all [...] Read more.
This article presents a method based on linear matrix inequalities (LMIs) for designing a modular feedback control law, whose synthesis guarantees the system stability, while switching to different network topologies. Such stability is achieved by means of a common Lyapunov function to all network admissible configurations. Several mechanisms to relieve the computational burden of this methodology in large-scale systems are also presented. To assess its applicability, the modular controller is tested on a real case study, namely the Barcelona drinking water network (DWN), and its performance is compared with that of other control strategies, showing the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Optimization and Control of Integrated Water Systems)
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18 pages, 2177 KiB  
Article
Optimal Water Management in Agro-Industrial Districts: An Energy Hub’s Case Study in the Southeast of Spain
by Jerónimo Ramos-Teodoro, Juan D. Gil, Lidia Roca, Francisco Rodríguez and Manuel Berenguel
Processes 2021, 9(2), 333; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9020333 - 12 Feb 2021
Cited by 7 | Viewed by 1667
Abstract
In this work, the optimal management of the water grid belonging to a pilot agro-industrial district, based on greenhouse cultivation, is analyzed. Different water supply plants are considered in the district, some of them using renewable energies as power sources, i.e., a solar [...] Read more.
In this work, the optimal management of the water grid belonging to a pilot agro-industrial district, based on greenhouse cultivation, is analyzed. Different water supply plants are considered in the district, some of them using renewable energies as power sources, i.e., a solar thermal desalination plant and a nanofiltration facility powered up by a photovoltaic field. Moreover, the trade with the water public utility network is also taken into account. As demanding agents, a greenhouse and an office building are contemplated. Due to the different water necessities, demand profiles, and the heterogeneous nature of the different plants considered as supplier agents, the management of the whole plant is not trivial. In this way, an algorithm based on the energy hubs approach, which takes into account economic terms and the optimal use of the available resources in its formulation, is proposed for the pilot district with a cropping area of 616 m2. Simulation results are provided in order to evidence the benefits of the proposed technique in two cases: Case 1 considers the flexible operation of the desalination plant, whereas in Case 2 the working conditions are forced to equal the plant’s maximum capacity (Case 2). A flexible operation results in a weekly improvement of 4.68% in profit, an optimized use of the desalination plant, and a reduction of the consumption of water from the public grid by 58.1%. Full article
(This article belongs to the Special Issue Optimization and Control of Integrated Water Systems)
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23 pages, 4700 KiB  
Article
Permeate Flux Control in SMBR System by Using Neural Network Internal Model Control
by Norhaliza Abdul Wahab, Nurazizah Mahmod and Ramon Vilanova
Processes 2020, 8(12), 1672; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8121672 - 17 Dec 2020
Cited by 3 | Viewed by 2097
Abstract
This paper presents a design of a data-driven-based neural network internal model control for a submerged membrane bioreactor (SMBR) with hollow fiber for microfiltration. The experiment design is performed for measurement of physical parameters from an actuator input (permeate pump voltage), which gives [...] Read more.
This paper presents a design of a data-driven-based neural network internal model control for a submerged membrane bioreactor (SMBR) with hollow fiber for microfiltration. The experiment design is performed for measurement of physical parameters from an actuator input (permeate pump voltage), which gives the information (outputs) of permeate flux and trans-membrane pressure (TMP). The palm oil mill effluent is used as an influent preparation to depict fouling phenomenon in the membrane filtration process. From the experiment, membrane fouling potential is observed from flux decline pattern, with a rapid increment of TMP (above 200 mbar). Membrane fouling is a complex process and the available models in literature are not designed for control system (filtration performance). Therefore, this work proposes an aeration fouling control strategy to measure the filtration performance. The artificial neural networks (Feed-Forward Neural Network—FFNN, Radial Basis Function Neural Network—RBFNN and Nonlinear Autoregressive Exogenous Neural Network—NARXNN) are used to model dynamic behaviour of flux and TMP. In this case, only flux is used in closed loop control application, whereby the TMP effect is used for monitoring. The simulation results show that reliable prediction of membrane fouling potential is obtained. It can be observed that almost all the artificial neural network (ANN) models have similar shape with the actual data set, with the highest accuracy of more than 90% for both RBFNN and NARXN. The RBFNN is preferable due to simple structure of the network. In the control system, the RBFNN IMC depicts the highest closed loop performance with only 3.75 s (settling time) for setpoint changes when compared with other controllers. In addition, it showed fast performance in disturbance rejection with less overshoot. In conclusion, among the different neural network tested configurations the one based on radial basis function provides the best performance with respect to prediction as well as control performance. Full article
(This article belongs to the Special Issue Optimization and Control of Integrated Water Systems)
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26 pages, 3321 KiB  
Article
Distributed Model Predictive Control Applied to a Sewer System
by Antonio Cembellín, Mario Francisco and Pastora Vega
Processes 2020, 8(12), 1595; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8121595 - 03 Dec 2020
Cited by 4 | Viewed by 1777
Abstract
In this work, a Distributed Model Predictive Control (MPC) methodology with fuzzy negotiation among subsystems has been developed and applied to a simulated sewer network. The wastewater treatment plant (WWTP) receiving this wastewater has also been considered in the methodology by means of [...] Read more.
In this work, a Distributed Model Predictive Control (MPC) methodology with fuzzy negotiation among subsystems has been developed and applied to a simulated sewer network. The wastewater treatment plant (WWTP) receiving this wastewater has also been considered in the methodology by means of an additional objective for the problem. In order to decompose the system into interconnected local subsystems, sectorization techniques have been applied based on structural analysis. In addition, a dynamic setpoint generation method has been added to improve system performance. The results obtained with the proposed methodology are compared to those obtained with standard centralized and decentralized model predictive controllers. Full article
(This article belongs to the Special Issue Optimization and Control of Integrated Water Systems)
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