Real-time control of combined sewer systems: Risks associated with uncertainties

Werf, Job Augustijn van der; Kapelan, Zoran; Langeveld, Jeroen

doi:10.1016/j.jhydrol.2022.128900

Cited by 13 publications

(8 citation statements)

References 48 publications

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“…The difference in performance for the WWTP Hoogvliet case as shown in Figure 12 was exclusively due to the pumped CSOs. The cause for the relative increase compared to the heuristic control is due to an overestimation in the runoff leading to the optimal setting calculated including the pre‐amptive switching on of the pumped CSOs.This loss induced through the rainfall‐runoff section of the internal MPC model is in apparent contrast with earlier findings of low levels of sensitivity to rainfall forecast uncertainty for MPC (Fiorelli et al., 2013; van der Werf et al., 2023). As the pumped CSOs cause overflows immediately when switched on, the self‐adjusting properties of MPC (which are the cause for the low impact of rainfall forecast uncertainty on the performance of MPC methods) are less pronounced.…”

Section: Resultscontrasting

confidence: 95%

“…This MPC architecture was previously used for the same catchment in van der Werf et al. (2023) and has showed to perform well for this catchment. A prediction horizon of 2 hr was selected, as this was the horizon for the rainfall nowcast data available.…”

Section: Case Studiesmentioning

confidence: 99%

“…This modeling decision means that formal optimization using linear or quadratic programming methods is not possible, given the non‐linear nature of the dynamics in this part of the sewer. Simplifying these dynamics would potentially lead to a loss of RTC efficacy due to increased uncertainties in the internal‐MPC model (Castelletti et al., 2023; van der Werf et al., 2023). As any effect the transitions might have on the RTC efficacy is due to increasing model uncertainties, additional (significantly large) uncertainties could mask any effects of these transition‐induced uncertainties.…”

Section: Case Studiesmentioning

confidence: 99%

“…Other optimization methods from MPC literature, particularly quadratic and linear programming, were not used given the importance of the non‐linear hydrodynamics in the transport sewer. As mentioned before, linearizing this part of the UDS would lead to an oversimplification of the dynamics in the internal‐MPC model which can lead to a loss in the performance of the MPC procedure due to uncertainties (van der Werf et al., 2023). The optimization runs were performed on a desktop PC with a four‐core Intel i5‐6500 CPU @ 3.20 GHz.…”

Section: Case Studiesmentioning

confidence: 99%

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The Impact of Blue‐Green Infrastructure and Urban Area Densification on the Performance of Real‐Time Control of Sewer Networks

Werf

Kapelan

Langeveld

2023

Water Resources Research

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Urban drainage systems (UDS) are designed to convey wastewater and urban runoff away from populated areas to treat the water and increasingly to recover valuable resources from it. To prevent flooding during rainfall events, combined sewer overflows (CSOs) are installed to discharge the excess of combined wastewater and urban runoff (i.e., stormwater) into a receiving water body. These CSO events can negatively impact the receiving water quality (Owolabi et al., 2022) and can be highly persistent and long-lasting in nature, for example, as in the case of micro-pollutants from CSO events impacting water resources (Mutzner et al., 2022). These effects are projected to be exacerbated by more intense rainfall events resulting from climate change (Gogien et al., 2022) and the expansion and densification of the population in existing urban areas.To minimize the adverse effects of CSOs, blue-green infrastructure in the form of sustainable urban drainage systems (SUDS) is increasingly implemented to revert to the pre-urbanized hydrological conditions by allowing more infiltration in the urban environment (Carter & Fowler, 2008). Allowing significant parts of the urban runoff to infiltrate or to be temporarily stored, alleviates pressure on the drainage system and can therefore significantly reduce the CSO volumes (e.g., Chen et al., 2019). Using these types of nature based solutions are increasingly mandated by legislative bodies. The new EU Urban Wastewater Treatment Directive, for example, explicitly states a preference for green developments and states that gray infrastructure should only be envisaged where absolutely necessary. In future (re)developments of the urban environment, different runoff patterns compared to the current situation are therefore highly likely.Another increasingly popular option to reduce CSOs is the use of Real-Time Control (RTC) procedures. RTC relies on real-time data obtained from the UDS to optimally control the system actuators (pumps and moveable gates) which has the potential to improve the UDS performance by activating redundant storage capacity in the system (Schütze et al., 2004). Various methods have been developed to achieve this, and can broadly be defined as heuristic and real-time optimization control methods (Garcia et al., 2015). Heuristic control methods are usually in the form of rule-based control, where a set of (optimized) rules are defined offline and implemented online.

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Section: Resultscontrasting

confidence: 95%

Section: Case Studiesmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

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The Impact of Blue‐Green Infrastructure and Urban Area Densification on the Performance of Real‐Time Control of Sewer Networks

Werf

Kapelan

Langeveld

2023

Water Resources Research

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“…Furthermore, an increased number of actuators to optimize results in a larger search space, which increases the computational time further. To enable centralized MPC for larger UDS, (partial) linearization or oversimplification of the UDS dynamics are often used but can introduce significant uncertainties into the internal‐MPC model, which can result in the loss of control performance potential (Van der Werf et al., 2023) or requires application of increasingly complex algorithms (e.g., Oh & Bartos, 2023; Svensen et al., 2021). As Naughton et al.…”

Section: Introductionmentioning

confidence: 99%

HAPPy to Control: A Heuristic And Predictive Policy to Control Large Urban Drainage Systems

Werf

Kapelan

Langeveld

2023

Water Resources Research

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Model Predictive Control (MPC) of Urban Drainage Systems (UDS) has been established as a cost‐effective method to reduce pollution. However, the operation of large UDS (containing over 20 actuators) can only be optimized by oversimplifying the UDS dynamics, potentially leading to a decrease in performance and reduction in users' trust, thus inhibiting widespread implementation of MPC procedures. A Heuristic And Predictive Policy (HAPPy) was set up, relying on the dynamic selection of the actuators with the highest impact on the UDS functioning and optimizing those in real‐time. The remaining actuators follow a pre‐set heuristic procedure. The HAPPy procedure was applied to two separate UDS in Rotterdam with the control objective being the minimization of overflow volume in each of the two cases. Results obtained show that the level of impact of the actuators on the UDS functioning changes during an event and can be predicted using a Random Forest algorithm. These predictions can be used to provide near‐global optimal actuator settings resulting in the performance of the HAPPy procedure that is comparable to a full‐MPC control and outperforming heuristic control procedures. The number of actuators selected to obtain near‐global optimal settings depends on the UDS and rainfall characteristics showing an asymptotic real‐time control (RTC) performance as the number of actuators increases. The HAPPy procedure showed different RTC dynamics for medium and large rainfall events, with the former showing a higher level of controllability than the latter. For medium events, a relatively small number of actuators suffices to achieve the potential performance improvement.

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Enhancing Water treatment predictions: a Machine Learning Approach with CNN and Water Wave optimization

Alkhadrawi,

Alzboon

2024

Asian J Civ Eng

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Real-time control of combined sewer systems: Risks associated with uncertainties

Cited by 13 publications

References 48 publications

The Impact of Blue‐Green Infrastructure and Urban Area Densification on the Performance of Real‐Time Control of Sewer Networks

The Impact of Blue‐Green Infrastructure and Urban Area Densification on the Performance of Real‐Time Control of Sewer Networks

HAPPy to Control: A Heuristic And Predictive Policy to Control Large Urban Drainage Systems

Enhancing Water treatment predictions: a Machine Learning Approach with CNN and Water Wave optimization

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