Neural-optimal control algorithm for real-time regulation of in-line storage in combined sewer systems

Darsono, Suseno; Labadie, John W.

doi:10.1016/j.envsoft.2006.09.005

Cited by 74 publications

(23 citation statements)

References 12 publications

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“…Such a modeling approach would turn the OCPs and SEPs into mixed integer nonlinear problems (MINLP) of very high computational burden in the case of large-scale systems. Therefore, this approach (although without integer variables) has only been applied to small network instances [Schwanenberg et al, 2010;Darsono and Labadie, 2007;Duchesne et al, 2003] or to irrigation channels with simple topologies [Xu et al, 2012;Sadowska et al, 2015].…”

Section: Sewer Network Modelmentioning

confidence: 99%

Output‐feedback control of combined sewer networks through receding horizon control with moving horizon estimation

Joseph-Duran

Ocampo‐Martínez

Cembraño

2015

Water Resources Research

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An output‐feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model‐based predictive control to large‐scale sewer networks, in‐spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control‐oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solving these problems, preliminary Receding Horizon Control with Moving Horizon Estimation (RHC/MHE) results, based on flow measurements, were also obtained. In this work, the RHC/MHE algorithm has been extended to take into account both flow and water level measurements and the resulting control loop has been extensively simulated to assess the system performance according different measurement availability scenarios and rain events. All simulations have been carried out using a detailed physically based model of a real case‐study network as virtual reality.

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Section: Sewer Network Modelmentioning

confidence: 99%

Output‐feedback control of combined sewer networks through receding horizon control with moving horizon estimation

Joseph-Duran

Ocampo‐Martínez

Cembraño

2015

Water Resources Research

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“…Meta-models commonly apply moving averages; Pleau et al (2005) minimize overflow and maximize sewer storage and conveyance through an online moving average model. Dynamic neural networks can also serve as approximations to the actual computational model (Darsono and Labadie, 2007).…”

Section: Introductionmentioning

confidence: 99%

Computationally Implicit Hydraulics for Real-Time Combined Sewer Overflow Modeling and Decision Support

Zimmer

Schmidt²,

Ostfeld

et al. 2012

World Environmental and Water Resources Congress 2012

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Large-scale combined sewer systems necessitate accurate hydraulic models with a low computational requirements to depict combined sewer overflows (CSOs) in real-time. A hydraulic model is proposed that incorporates the mass and momentum equations into a series of look-up tables. Flow that enters the interceptors is routed downstream based on a hydraulic performance graph (HPG) which conserves momentum, and a volumetric performance graph (VPG) which conserves mass, established for each conduit. Weirs and sluice gates that control water distribution throughout the combined sewer system are also represented by look-up tables created off-line. During real-time computations, referencing the look-up tables is faster than computing the full equations. The model includes accurate sewer and deep tunnel components imported from Arc-GIS, and is shown to emulate EPA SWMM 5.0 dynamic wave results on a faster time scale. Calibration and timing results show that the model may be successfully applied to evaluate potential operating scenarios more quickly than SWMM.

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“…The main goal to be achieved in DWNs is to reduce pumping costs -for instance, by filling tanks in low tariff periods -while maintaining adequate system pressure to meet fluctuating consumer demands [12]. Similarly, in urban drainage management, the goals are to minimize flooding and combined sewer overflow to the receiving environment (CSO) by controlling flow within the wastewater system, through for example, inline storage [13] or using underground detention tanks, gates and pumps [6], [14].…”

Section: Application Of Predictive Control Strategies To the Managemementioning

confidence: 99%

Application of predictive control strategies to the management of complex networks in the urban water cycle [Applications of Control]

et al. 2013

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The management of the urban water cycle (UWC) is a subject of increasing interest because of its social, economic and environmental impact. The most important issues include the sustainable use of limited resources and the reliability of service to consumers with adequate quality and pressure levels, as well as the urban drainage management to prevent flooding and polluting discharges to the environment.Climate change is expected to produce regional changes in water availability in the 21st century. For example, Northern and Southern Europe are expected to experience, respectively, an increase and a decrease in mean precipitation, as well as an increase in the magnitude and frequency of extreme events [1]. These changes will have direct consequences through impacts on the availability and quality of water in the water cycle. Optimal management strategies for the systems in the water cycle can contribute to reduce the vulnerability of urban water systems (UWS) to climatic variability and change.An UWC is mainly comprised of the following systems: (i) Supply/production: water supply from superficial or underground sources and treatment to achieve necessary quality levels, (ii) transport networks, which use natural or artificial open-flow channels and/or pressurized conduits to deliver water from the treatment plants to the consumer areas, (iii) water distribution to consumers, involving pressurized pipeline networks, storage tanks, booster pumps and pressure/flow control valves, (iv) urban drainage and sewer systems carrying waste-and rain water together to wastewater treatment plants (WWTP), before returning it to the receiving environment. In urban environments, drinking water is provided by means of a drinking water network (DWN) to consumers and industry, and sanitation/urban drainage is achieved through a sewer network (SN). In a large number of cities, DWNs are managed using telemetry and telecontrol systems which provide, in real time, pressure, flow, quality and other measurements at several key locations within the network. Flow, pressure and storage control elements are operated from a central dispatch in a centralized or decentralized scheme.In some cases, advanced urban drainage systems also include sewage control infrastructure, such as detention tanks, pumps, gates and weirs. All these elements are monitored and controlled by using telemetry/telecontrol systems, which involve rain-gauge networks, wastewater level and/or flow meters in the sewers and actuators at the valves, pumps and weirs, a communication network, and monitory and control software. The control system manages the flows and the storage in the network in order to minimize the risk of untreated water overflows to the streets or to the receiving environment.The use of optimal control for managing water systems to achieve energy efficiency, cost minimization and environmental protection is summarized in this article. Applying optimal control concepts to water systems requires the development of control-oriented dynamic models to represent...

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Neural-optimal control algorithm for real-time regulation of in-line storage in combined sewer systems

Cited by 74 publications

References 12 publications

Output‐feedback control of combined sewer networks through receding horizon control with moving horizon estimation

Output‐feedback control of combined sewer networks through receding horizon control with moving horizon estimation

Computationally Implicit Hydraulics for Real-Time Combined Sewer Overflow Modeling and Decision Support

Application of predictive control strategies to the management of complex networks in the urban water cycle [Applications of Control]

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