Conference Agenda

The city of Rotterdam is ambitious to develop towards a climate adaptive, circular city and invests heavily in blue-green solutions, such as green roofs, urban agriculture, floating parks and water squares. As the transition to blue-green and climate proof systems will take decades, Rotterdam is also investing in Real Time Control (RTC) to optimise the performance of the existing wastewater infrastructure. In 2018, preparations started to develop CAS2.0, a new RTC system benefitting from the strong development of the sewer monitoring network in Rotterdam as well as research developments in the field of RTC. In 2023, full scale implementation RTC system started with SAT (site acceptance test) finished in June 2024 for sewer systems connected to wastewater treatment plant (wwtp) Hoogvliet, October 2024 for sewer systems connected to wwtp Kralingseveer and scheduled in February 2025 for sewers connected to wwtp Dokhaven. This abstract describes the use of sewer and wwtp models to develop control rules, the evaluation system to evaluate performance of the wastewater system and the CAS2.0 RTC system and the first results of full scale implementation.

An experimental set-up to validate the efficacy of real-time control procedures and algorithms is presented. This set-up was used to assess the effect of operational sensor uncertainty beyond random noise. The efficacy of real-time control to reduce CSO volumes and avoid negative side effects under such conditions was assessed experimentally. Depending on the type of sensor error induced, the response of the system was either moderate efficacy loss or potential increased flooding compared to a baseline. This highlights the need to consider methods beyond sensor redundancy to ensure the long term robustness of real-time control strategies under sensor uncertainty.

This study compares two approaches to real-time control (RTC) of rainwater harvesting (RWH) cisterns using five years of full-scale data. The first approach, a smart detention logic, empties cisterns 24 hours after storms, while the second, a rainwater harvesting logic, partially drains cisterns in anticipation of forecasted storms. We assess the performance of these logics in optimizing storage capacity and minimizing overflow. Additionally, we quantify the accuracy of the weather forecast used, analysing the relationship between forecast accuracy and cistern performance. Our findings reveal that underestimated storms tend to be larger and longer, while overestimated storms are smaller and shorter in duration.

A large-scale 1D/2D coupled model is being developed for the Barcelona Metropolitan Area. It aims to enhance the urban flood modeling capabilities for the region as a tool to improve preparedness against flash flood risks. The model employs a hybrid approach to runoff routing, integrating both semi-distributed and fully distributed methods to accurately simulate runoff behavior in urbanized environments. Key elements include the representation of drainage systems, particularly the role of grate inlets, which facilitate water exchange between different domains. The model generates flood maps that depict water depth and velocity allowing to quantify the damage on infrastructure and services, including direct and indirect tangible impacts as well as intangible risk for people. A significant challenge addressed in this research is the data gap in sewer network information, as only 25 out of 36 municipalities have available data. To overcome this limitation, a synthetic sewer network generation methodology has been developed, utilizing geographic information and structural parameters to approximate sewer layouts. This innovative data gap filling methodology enhances the possibility to develop 1D/2D coupled models under scarce data conditions. The methodology has been tested with satisfactory results in a municipality of the area of study.

The Sewer Department of the city of Warsaw, Poland in collaboration with ITWH GmbH restructures and expands the sewer network. The objective of the initiative is to reduce combined sewer overflow (CSO) volume and limit the number of registered CSO events. A real-time-control system is established to facilitate centralised control of pumping stations and storage channels, thereby ensuring efficient utilisation of retention space. A digital hydraulic twin of the sewer network is developed. This tool allows real-time access to current conditions and predictions of hydraulic conditions up to two hours into the future. The creation of a fine-resolution precipitation forecast is facilitated by the incorporation of radar, rain gauge, and disdrometer data, which serves as an input for the hydraulic prediction feature. Real-time data of water level and flow measurements is directly used in the digital twin for an online calibration of the sewer network through the adjustment of water levels. The accuracy of the volume prediction is heavily dependent on the initial calibration and is repeatedly affected by changes in the sewer network, as manual readjustment is necessary.

Active control of pump stations in urban drainage tunnels poses a major challenge for operators, given that pump schedules must be planned pre-emptively to avert overflows while minimizing energy costs. This study derives and implements a model predictive control (MPC) scheme to determine the optimal pumping schedule for a proposed tunnel system in Austin, Texas. The proposed system consists of an 8.4 km long, 6.7 m diameter drainage tunnel that must pump water at 8.5 m³/s into a nearby river to avoid surcharging during storms. To determine the optimal pump schedule, we first develop a hydraulic model for the tunnel system based on the Saint-Venant equations for unsteady flow. We then derive and implement an MPC program that determines the optimal pump schedule while balancing between flood control and energy savings. We find that the MPC algorithm completely averts flooding during a 25-year storm event when flood control is prioritized. Conversely, when energy savings are prioritized, the MPC control strategy saves roughly 30 GJ in pump energy expenditures. Importantly, this study demonstrates a working implementation of MPC using a full physically-based model of an urban drainage system, indicating that optimal control schemes need not sacrifice model fidelity for real-time applications.