On modelling approaches for receding-horizon control design applied to large-scale sewage systems

Ocampo‐Martínez, Carlos; Puig, Vicenç

doi:10.1109/cdc.2009.5400216

Cited by 5 publications

(8 citation statements)

References 18 publications

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“…Urban drainage systems are most often controlled by passive control, rule-based (local) control, or manually by an operator. However, better control can usually be achieved by using global RTC (Cen and Xi, 2009;Giraldo et al, 2010;Leirens et al, 2010;L€ owe et al, 2016;Papageorgiou, 1998, 1999;Ocampo-Martinez and Puig, 2009a;Papageorgiou, 1983, 1988, Pleau et al, 1996, 2005, Rauch and Harremo€ es, 1996. Hence, it is likely that suboptimal control is currently implemented in many places.…”

Section: Control Of Urban Drainage Systems In Generalmentioning

confidence: 99%

“…However, by introducing non-linearities into the model, the optimization may converge towards a local optimum. Therefore, the enhanced performance should be weighed against the loss of optimality occurring because a global optimum is not guaranteed (Joseph-Duran et al, 2014b;Marinaki and Papageorgiou, 1998;Ocampo-Martinez et al, 2007Papageorgiou, 1988) and against the increased computational time (Ocampo-Martinez and Puig, 2009a). Furthermore, it is important that the improvements obtained from including non-linearities are large compared to the errors stemming from the uncertain rainfall-runoff predictions .…”

Section: Non-linear Programsmentioning

confidence: 99%

“…Instead, simpler models are used; however, there is much less consensus on how to construct lumpedconceptual models and even less on internal MPC models. Finding an appropriate model description that captures the important dynamics of the system is one of the most important tasks when designing RTC for urban drainage systems, as this accuracy is directly reflected in the quality of the optimized control (Joseph-Duran et al, 2014a, Ocampo-Martinez and Puig, 2009aPapageorgiou, 1983;Pleau et al, 1996;Sch€ utze et al, 2004). This trade-off between computational cost and the accuracy of the model is quantified by Mollerup (2015), who showed that more detailed models have better model performances, but also longer computational times.…”

Section: Internal Mpc Modelsmentioning

confidence: 99%

“…Modeled as simple summations (merging flow) or diversions into predefined ratios (splitting flow). Ocampo-Martinez and Puig (2009a) provided an example of a merging flow:…”

Section: Simple Pipe Junctionsmentioning

confidence: 99%

See 3 more Smart Citations

Model predictive control of urban drainage systems: A review and perspective towards smart real-time water management

Lund

Falk

Borup

et al. 2018

Critical Reviews in Environmental Science and Technology

142

108

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Model predictive control (MPC) can be used to manage combined urban drainage systems more efficiently for protection of human health and the environment, but examples of operational implementations are rare. This paper reviews more than 30 years of partly heterogeneous research on the topic. We propose a terminology for MPC of urban drainage systems and a hierarchical categorization where we emphasize four overall components: the "receding horizon principle", the "optimization model", the "optimization solver", and the "internal MPC model". Most of the reported optimization models share the trait of a multiobjective optimization based on a conceptual internal MPC model. However, there is a large variety of both convex and non-linear optimization models and optimization solvers as well as constructions of the internal MPC model. Furthermore, literature disagrees about the optimal length of the components in the receding horizon principle. The large number of MPC formulations and evaluation approaches makes it problematic to compare different MPC methods. This review highlights methods, challenges, and research gaps in order to make MPC of urban drainage systems accessible for researchers and practitioners from different disciplines. This will pave the way for shared understanding and further development within the field, and eventually lead to more operational implementations.

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Section: Control Of Urban Drainage Systems In Generalmentioning

confidence: 99%

Section: Non-linear Programsmentioning

confidence: 99%

Section: Internal Mpc Modelsmentioning

confidence: 99%

“…Modeled as simple summations (merging flow) or diversions into predefined ratios (splitting flow). Ocampo-Martinez and Puig (2009a) provided an example of a merging flow:…”

Section: Simple Pipe Junctionsmentioning

confidence: 99%

See 2 more Smart Citations

Model predictive control of urban drainage systems: A review and perspective towards smart real-time water management

Lund

Falk

Borup

et al. 2018

Critical Reviews in Environmental Science and Technology

142

108

View full text Add to dashboard Cite

show abstract

“…The increasing challenges in the urban drainage sector caused by climate change, stricter environmental regulations and growing urbanisation, have triggered a need for online models to be used for warning and control purposes (see, for example, (Krämer et al, 2007;Ocampo-Martinez and Puig, 2009;Puig et al, 2009;Giraldo et al, 2010)). However, the inherent uncertainties associated with the model predictions are rarely accounted for, although there seems to be a consensus from several sources regarding uncertainty in modelling, prediction and simulation with urban drainage models (Lei and Schilling, 1996;Willems and Berlamont, 2002;Kuczera et al, 2006;Kleidorfer et al, 2009;Freni and Mannina, 2010;Deletic et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Grey‐box modelling of flow in sewer systems with state‐dependent diffusion

et al. 2011

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Generating flow forecasts with uncertainty limits from rain gauge inputs in sewer systems require simple models with identifiable parameters that can adequately describe the stochastic phenomena of the system. In this paper, a simple grey-box model is proposed that is attractive for both forecasting and control purposes. The grey-box model is based on stochastic differential equations and a key feature is the separation of the total noise into process and measurement noise. The grey-box approach is properly introduced and hypothesis regarding the noise terms are formulated. Three different hypotheses for the diffusion term are investigated and compared: one that assumes additive diffusion; one that assumes state proportional diffusion; and one that assumes state exponentiated diffusion. To implement the state dependent diffusion terms Itô's formula and the Lamperti transform are applied. It is shown that an additive diffusion noise term description leads to a violation of the physical constraints of the system, whereas a state dependent diffusion noise avoids this problem and should be favoured. It is also shown that a logarithmic transformation of the flow measurements secures positive lower flow prediction limits, because the observation noise is proportionally scaled with the modelled output. Finally it is concluded that a state proportional diffusion term best and adequately describes the one-step flow prediction uncertainty, and a proper description of the system noise is important for ascertaining the physical parameters in question.

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