Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning

Wastewater treatment plants are designed to eliminate pollutants and alleviate environmental pollution. However, the construction and operation of WWTPs consume resources, emit greenhouse gases (GHGs) and produce residual sludge, thus require further optimization. WWTPs are complex to control and optimize because of high nonlinearity and variation. This study used a novel technique, multi-agent deep reinforcement learning, to simultaneously optimize dissolved oxygen and chemical dosage in a WWTP. The reward function was specially designed from life cycle perspective to achieve sustainable optimization. Five scenarios were considered: baseline, three different effluent quality and cost-oriented scenarios. The result shows that optimization based on LCA has lower environmental impacts compared to baseline scenario, as cost, energy consumption and greenhouse gas emissions reduce to 0.890 CNY/m3-ww, 0.530 kWh/m3-ww, 2.491 kg CO2-eq/m3-ww respectively. The cost-oriented control strategy exhibits comparable overall performance to the LCA driven strategy since it sacrifices environmental bene ts but has lower cost as 0.873 CNY/m3-ww. It is worth mentioning that the retrofitting of WWTPs based on resources should be implemented with the consideration of impact transfer. Specifically, LCA SW scenario decreases 10 kg PO4-eq in eutrophication potential compared to the baseline within 10 days, while significantly increases other indicators. The major contributors of each indicator are identified for future study and improvement. Last, the author discussed that novel dynamic control strategies required advanced sensors or a large amount of data, so the selection of control strategies should also consider economic and ecological conditions

A decomposition-based multiobjective evolutionary algorithm with angle-based adaptive penalty

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.A multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem (MOP) into a number of scalar optimization subproblems and optimizes them in a collaborative manner. In MOEA/D, decomposition mechanisms are used to push the population to approach the Pareto optimal front (POF), while a set of uniformly distributed weight vectors are applied to maintain the diversity of the population. Penalty-based boundary intersection (PBI) is one of the approaches used frequently in decomposition. In PBI, the penalty factor plays a crucial role in balancing convergence and diversity. However, the traditional PBI approach adopts a fixed penalty value, which will significantly degrade the performance of MOEA/D on some MOPs with complicated POFs. This paper proposes an angle-based adaptive penalty (AAP) scheme for MOEA/D, called MOEA/D-AAP, which can dynamically adjust the penalty value for each weight vector during the evolutionary process. Six newly designed benchmark MOPs and an MOP in the wastewater treatment process are used to test the effectiveness of the proposed MOEA/D-AAP. Comparison experiments demonstrate that the AAP scheme can significantly improve the performance of MOEA/D

Lost in optimisation of water distribution systems? A literature review of system design

This is the final version of the article. Available from MDPI via the DOI in this record.Optimisation of water distribution system design is a well-established research field, which has been extremely productive since the end of the 1980s. Its primary focus is to minimise the cost of a proposed pipe network infrastructure. This paper reviews in a systematic manner articles published over the past three decades, which are relevant to the design of new water distribution systems, and the strengthening, expansion and rehabilitation of existing water distribution systems, inclusive of design timing, parameter uncertainty, water quality, and operational considerations. It identifies trends and limits in the field, and provides future research directions. Exclusively, this review paper also contains comprehensive information from over one hundred and twenty publications in a tabular form, including optimisation model formulations, solution methodologies used, and other important details

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

Modeling and real-time control of urban drainage systems: A review

Urban drainage systems (UDS) may be considered large-scale systems given their large number of associated states and decision actions, making challenging their real-time control (RTC) design. Moreover, the complexity of the dynamics of the UDS makes necessary the development of strategies for the control design. This paper reviews and discusses several techniques and strategies commonly used for the control of UDS. Moreover, the models to describe, simulate, and control the transport of wastewater in UDS are also reviewed.This work has been partially supported by Mexichem, Colombia through the project “Drenaje Urbano y Cambio Climático: Hacia los Sistemas de Alcantarillado del Futuro.” Fase II, with reference No. 548-2012, the scholarships of Colciencias No. 567-2012 and 647-2013, and the project ECOCIS (Ref. DPI2013-48243-C2-1-R).Peer Reviewe

Comparison of optimisation algorithms for centralised anaerobic co-digestion in a real river basin case study in Catalonia

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.Peer ReviewedPostprint (published version

Control and Automation

Control and automation systems are at the heart of our every day lives. This book is a collection of novel ideas and findings in these fields, published as part of the Special Issue on Control and Automation. The core focus of this issue was original ideas and potential contributions for both theory and practice. It received a total number of 21 submissions, out of which 7 were accepted. These published manuscripts tackle some novel approaches in control, including fractional order control systems, with applications in robotics, biomedical engineering, electrical engineering, vibratory systems, and wastewater treatment plants. This Special Issue has gathered a selection of novel research results regarding control systems in several distinct research areas. We hope that these papers will evoke new ideas, concepts, and further developments in the field

On the modeling and real-time control of urban drainage systems: A survey

Trabajo presentado a la 11th International Conference on Hydroinformatics celebrada en New York (US) del 17 al 21 de agosto de 2014.Drainage networks are complex systems composed by several processes including recollection, transport, storing, treatment, and releasing the water to a receiving environment. The way Urban Drainage Systems (UDS) manage wastewater is through the convenient handling of active elements such as gates (redirection and/or retention), storing tanks, and pumping stations, when needed. Therefore, modeling and control of UDS basically consists in knowing and representing the (dynamical) behavior of these elements and managing them properly in order to achieve a given set of control objectives, such as minimization of flooding in streets or maximization of treated wastewater in the system. Given the large number of elements composing an UDS and the interaction between them, management and control strategies may depend on highly complex system models, which implies the explicit difficulty for designing real-time control (RTC) strategies. This paper makes a review of the models used to describe, simulate, and control UDS, proposes a revision of the techniques and strategies commonly used for the control UDS, and finally compares several control strategies based on a case study.This work has been partially supported by project N°548-2012 “Drenaje Urbano y Cambio Climático: Hacia los Sistemas de Alcantarillado del Futuro.”, Mexichem Colombia S.A, the scholarships of Colciencias N°567-2012 and 647-2013, and the EU Project EFFINET (FP7-ICT-2011-8-31855) and the DGR of Generalitat de Catalunya (SAC group Ref. 2009/SGR/1491).Peer Reviewe

Instrumentation and control of anaerobic digestion processes: a review and some research challenges

The final publication is available at Springer via http://dx.doi.org/10.1007/s11157-015-9382-6[EN] To enhance energy production from methane or resource recovery from digestate, anaerobic digestion processes require advanced instrumentation and control tools. Over the years, research on these topics has evolved and followed the main fields of application of anaerobic digestion processes: from municipal sewage sludge to liquid mainly industrial then municipal organic fraction of solid waste and agricultural residues. Time constants of the processes have also changed with respect to the treated waste from minutes or hours to weeks or months. Since fast closed loop control is needed for short time constant processes, human operator is now included in the loop when taking decisions to optimize anaerobic digestion plants dealing with complex solid waste over a long retention time. Control objectives have also moved from the regulation of key variables measured online to the prediction of overall process perfor- mance based on global off-line measurements to optimize the feeding of the processes. Additionally, the need for more accurate prediction of methane production and organic matter biodegradation has impacted the complexity of instrumentation and should include a more detailed characterization of the waste (e.g., biochemical fractions like proteins, lipids and carbohydrates)andtheirbioaccessibility andbiodegradability characteristics. However, even if in the literature several methodologies have been developed to determine biodegradability based on organic matter characterization, only a few papers deal with bioaccessibility assessment. 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