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

On The Modeling And Real-Time Control Of Urban Drainage Systems: A Survey

Drainage network are complex systems composed by several processes including recollection, transport, storing, wastewater and/or rain treatment, and return of the water to a receiving environment. Urban drainage systems (UDS) involve most of these processes inside cities and can be either separate or combined systems, depending on how wastewater and rainwater are managed. The way UDS manage the wastewater is through the convenient handling of active elements such as gates (redirection and/or retention), storing tanks and pumping stations, when needed. Therefore, the modeling and control of UDS basically consists in knowing and representing the (dynamical) behavior of those elements and manage 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 a UDS and the interaction between them, management and control strategies may depend on highly complex system models, what implies the explicit difficulty for designing real-time control strategies. This paper makes a review on the huge world of models used to describe, simulate, and control UDS. Moreover, a revision of the techniques and strategies commonly used for the control of these systems is also presented and discussed. Mechanisms that ensure the correct operation of the UDS under presence of failures or communication flaws in the system are considered as well

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

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.Peer ReviewedPostprint (author’s final draft

Indicator based multi-criteria decision support systems for wastewater treatment plants

Data availability: Data will be made available on request.Wastewater treatment plant decision makers face stricter regulations regarding human health protection, environmental preservation, and emissions reduction, meaning they must improve process sustainability and circularity, whilst maintaining economic performance. This creates complex multi-objective problems when operating and selecting technologies to meet these demands, resulting in the development of many decision support systems for the water sector. European Commission publications highlight their ambition for greater levels of sustainability, circularity, and environmental and human health protection, which decision support system implementation should align with to be successful in this region. Following the review of 57 wastewater treatment plant decision support systems, the main function of multi-criteria decision-making tools are technology selection and the optimisation of process operation. A large contrast regarding their aims is found, as process optimisation tools clearly define their goals and indicators used, whilst technology selection procedures often use vague language making it difficult for decision makers to connect selected indicators and resultant outcomes. Several recommendations are made to improve decision support system usage, such as more rigorous indicator selection protocols including participatory selection approaches and expansion of indicators sets, as well as more structured investigation of results including the use of sensitivity or uncertainty analysis, and error quantification.Horizon 2020 research and innovation programme DEEP PURPLE. The H2020 DEEP PURPLE project has received funding from the Bio-based Industries Joint Undertaking (JU) under the European Union's Horizon 2020 research and innovation programme under grant agreement No 837998. The JU receives support from the European Union's Horizon 2020 research and innovation programme and the Bio-based Industries Consortium

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

Gasification for Practical Applications

Although there were many books and papers that deal with gasification, there has been only a few practical book explaining the technology in actual application and the market situation in reality. Gasification is a key technology in converting coal, biomass, and wastes to useful high-value products. Until renewable energy can provide affordable energy hopefully by the year 2030, gasification can bridge the transition period by providing the clean liquid fuels, gas, and chemicals from the low grade feedstock. Gasification still needs many upgrades and technology breakthroughs. It remains in the niche market, not fully competitive in the major market of electricity generation, chemicals, and liquid fuels that are supplied from relatively cheap fossil fuels. The book provides the practical information for researchers and graduate students who want to review the current situation, to upgrade, and to bring in a new idea to the conventional gasification technologies

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.Peer ReviewedPostprint (author's final draft

Assessing the Viability of Heuristic Predictive Control for Integrated Urban Drainage Systems

The implementation of real time control (RTC) in integrated urban drainage systems (IUDS) has been extensively explored in numerous studies, with the purpose of improving its performance, particularly, during storm occurrences. This approach frequently focuses on volume-based control, to minimize combined sewer overflows (CSOs) volume and investment costs in CSO controlling and new infrastructures intended to manage these incidents and mitigate polluted discharges into the receiving watercourses. Among the different RTC strategies, heuristic and optimization-based control can be distinguished from the research work available, such as rule based RTC (RB-RTC) and model predictive control (MPC), respectively. To enhance the viability of RTC, rainfall forecasting has been introduced in the IUDS, to assess the possible combination with RTC and the benefits and risks that derive from it, considering these forecasts are associated with uncertainties. Despite the reasonable results obtained for both control strategies in CSO controlling, only optimization-based control has been combined with rainfall forecasts. This dissertation assesses the potential of heuristic predictive control in IUDS, by combining RB RTC with real radar rainfall forecast and applying it to a case study in the Netherlands. An existent full-integrated catchment model built for the IUDS selected for this study was used and sufficiently calibrated to deliver reasonable results compared with monitoring data. The accuracy of the real radar rainfall forecast was evaluated and, when compared with observed rainfall data, it correctly predicts a considerable amount of storm occurrences. One of the two heuristic control strategies developed proved to be beneficial for the performance of the IUDS, contributing for CSO volume reduction and avoiding the overcharge of the wastewater treatment plant (WWTP). This can potentially increase the quality of the receiving watercourses, prevent urban flooding and maximize the efficiency of the WWTP operation. Finally, recommendations, to further improve and explore heuristic predictive control, are provided.A implementação de controlo em tempo real (RTC) nos sistemas de drenagem urbanos integrados (IUDS) tem sido investigada com o propósito de melhorar o seu desempenho, particularmente, durante eventos de precipitação. Esta abordagem baseia-se maioritariamente na minimização do volume das descargas de emergência (CSOs) e dos custos de investimento no controlo de CSO e em novas infraestruturas projetadas para mitigar estas ocorrências e a deterioração dos emissários. As estratégias de RTC podem ser fundamentalmente baseadas em controlo heurístico e de otimização, distinguindo-se o RTC baseado em regras (RB-RTC) e modelo de controlo preditivo (MPC), respetivamente. Embora esteja associada a incertezas, a previsão de precipitação foi introduzida em IUDS para investigar a sua combinação com o RTC, nomeadamente os benefícios e os riscos. Estas estratégias apresentam resultados razoáveis relativamente ao controlo de CSO, mas, apenas as de otimização foram aplicadas com previsões. Esta dissertação avalia o potencial do controlo preditivo heurístico em IUDS, através da aplicação de RB-RTC com previsão de precipitação por radar num estudo de caso nos Países Baixos. Para isso, um modelo de drenagem urbana desenvolvido para o IUDS selecionado para este estudo foi utilizado e suficientemente calibrado para produzir resultados razoáveis, comparativamente a medições de monitorização. A precisão da previsão também foi avaliada e comparada com medições, e a mesma prevê corretamente um número considerável de eventos de precipitação. Uma das duas estratégias de controlo heurístico desenvolvidas demonstrou constituir um benefício para um melhor desempenho dos IUDS, uma vez que contribui para a redução do volume de CSO e evita a sobrecarga da estação de tratamento de águas residuais (WWTP). Esta estratégia pode também contribuir para um aumento da qualidade dos emissários, prevenir inundações urbanas e maximizar a eficiência da operação das WWTP. Por fim, são disponibilizadas recomendações para investigar e melhorar o controlo preditivo heurístico

Real-Time Substrate Feed Optimization of Anaerobic Co-Digestion Plants

In anaerobic co-digestion plants a mix of organic materials is converted to biogas using the anaerobic digestion process. These organic materials, called substrates, can be crops, sludge, manure, organic wastes and many more. They are fed on a daily basis and significantly affect the biogas production process. In this thesis dynamic real-time optimization of the substrate feed for anaerobic co-digestion plants is developed. In dynamic real-time optimization a dynamic simulation model is used to predict the future performance of the controlled plant. Therefore, a complex simulation model for biogas plants is developed, which uses the famous Anaerobic Digestion Model No. 1 (ADM1). With this model the future economics as well as stability can be calculated resulting in a multi-objective performance criterion. Using multi-objective nonlinear model predictive control (NMPC) the model predictions are used to find the optimal substrate feed for the biogas plant. Therefore, NMPC solves an optimization problem over a moving horizon and applies the optimal substrate feed to the plant for a short while before recalculating the new optimal solution. The multi-objective optimization problem is solved using state-of-the-art methods such as SMS-EMOA and SMS-EGO. The performance of the proposed approach is validated in a detailed simulation studyAlgorithms and the Foundations of Software technolog

Optimisation of a water company’s waste pumping asset base with a focus on energy reduction

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWater companies use a significant quantity of electricity for the operation of their clean and wastewater assets. Rising energy prices have led to higher energy bills within the water companies, which has increased operating costs. Thus, improvements in demand side energy management are needed to increase efficiency and reduce costs, which forms the premise for this research project. Thames Water Utilities Ltd has identified that improvements in demand side energy management is required and is currently researching various methods to reduce energy consumption. One initiative included the upgrade of a variety of site telemetry assets. By deploying these new telemetry assets, Thames Water Utilities Ltd are more able to liberate the asset data and as such, be able to make informed decisions on how better to control and optimise the target sites, which is where this research project has seen further opportunities. This enhanced telemetry and SCADA infrastructure will enable successful research to further develop an intelligent integrated system that tackles pump scheduling and process control with the emphasis on energy management. The use of modern techniques, such as artificial intelligence, to optimise the network operation is gradually gaining traction. The balance between implementing new technology (with the benefits it may bring) and reluctance to change from the incumbent operating model will always provide challenges in the technology adoption agenda. The main work of this research project included the physical surveying of a wastewater hydraulic catchment, inclusive of all wet well dimensions, lidar overlays, and pump electrical power characteristics. These survey results where then able to be programmed by the research into the company’s' hydraulic model to enable a higher degree of accuracy in the modelling, as well as enabling electrical power as a measurable output. From here, the model was then able to be optimised, focussing on electrical energy as an output variable for reduction. The research concluded that electrical energy consumption over time can be reduced using the aforementioned strategies and as such recommends further work to move from the model environment to physical architecture. It does so with the key message that risk tolerances on water levels must be pre-agreed with hydraulic specialists prior to deployment