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Processes
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Optimization and Control of Integrated Water Systems (Volume II)
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Optimization and Control of Integrated Water Systems (Volume II)

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 2229

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 and reducing the final waste to a minimum, as well as generating economic opportunities.

In particular, these issues apply to water systems, as they are clear candidates for improving water quality, safety, and reliability while minimizing energy consumption and gas emissions. Although, traditionally, wastewater treatment plants (WWTP), sewage systems, and the receiving water bodies have been considered as separate systems, they are, in fact, 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 the coordinated control of the IWS. Moreover, advanced control and supervision methodologies can be used within a circular economy framework to offer solutions and 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 in IWS analysis.

This Special Issue on the "Optimization and Control of Integrated Water Systems (Volume II)" 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 integrated water systems as a whole.

Topics may 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 the 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.

Prof. Dr. Pastora Isabel Vega Cruz
Prof. Dr. 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
  • game theory
  • data-driven fault detection, diagnosis and prognosis methods
  • deep learning
  • integrated water systems (IWS)

Published Papers (2 papers)

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Research

25 pages, 6549 KiB  
Article
Intelligent Control of Wastewater Treatment Plants Based on Model-Free Deep Reinforcement Learning
by Oscar Aponte-Rengifo, Mario Francisco, Ramón Vilanova, Pastora Vega and Silvana Revollar
Processes 2023, 11(8), 2269; https://doi.org/10.3390/pr11082269 - 28 Jul 2023
Viewed by 1099
Abstract
In this work, deep reinforcement learning methodology takes advantage of transfer learning methodology to achieve a reasonable trade-off between environmental impact and operating costs in the activated sludge process of Wastewater treatment plants (WWTPs). WWTPs include complex nonlinear biological processes, high uncertainty, and [...] Read more.
In this work, deep reinforcement learning methodology takes advantage of transfer learning methodology to achieve a reasonable trade-off between environmental impact and operating costs in the activated sludge process of Wastewater treatment plants (WWTPs). WWTPs include complex nonlinear biological processes, high uncertainty, and climatic disturbances, among others. The dynamics of complex real processes are difficult to accurately approximate by mathematical models due to the complexity of the process itself. Consequently, model-based control can fail in practical application due to the mismatch between the mathematical model and the real process. Control based on the model-free reinforcement deep learning (RL) methodology emerges as an advantageous method to arrive at suboptimal solutions without the need for mathematical models of the real process. However, convergence of the RL method to a reasonable control for complex processes is data-intensive and time-consuming. For this reason, the RL method can use the transfer learning approach to cope with this inefficient and slow data-driven learning. In fact, the transfer learning method takes advantage of what has been learned so far so that the learning process to solve a new objective does not require so much data and time. The results demonstrate that cumulatively achieving conflicting objectives can efficiently be used to approach the control of complex real processes without relying on mathematical models. Full article
(This article belongs to the Special Issue Optimization and Control of Integrated Water Systems (Volume II))
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25 pages, 6503 KiB  
Article
Optimal Operation of a Benchmark Simulation Model for Sewer Networks Using a Qualitative Distributed Model Predictive Control Algorithm
by Antonio Cembellín, Mario Francisco and Pastora Vega
Processes 2023, 11(5), 1528; https://doi.org/10.3390/pr11051528 - 17 May 2023
Cited by 1 | Viewed by 824
Abstract
This article presents a distributed model predictive control algorithm including fuzzy negotiation among subsystems and a dynamic setpoint generation method, applied to a simulated sewerage network. The methodology considers WWTP as an additional objective of control. To improve the performance of a DMPC [...] Read more.
This article presents a distributed model predictive control algorithm including fuzzy negotiation among subsystems and a dynamic setpoint generation method, applied to a simulated sewerage network. The methodology considers WWTP as an additional objective of control. To improve the performance of a DMPC using a hydraulic model for prediction, a more detailed model has been considered including suspended solids concentration (TSS). The results obtained with the proposed methodology have been validated on a benchmark simulation model for sewer systems developed to test and compare methodologies, showing good performance. Full article
(This article belongs to the Special Issue Optimization and Control of Integrated Water Systems (Volume II))
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