Conference Agenda

The main goal of Urban Drainage Systems (UDS) is to properly handle rainwater, surface runoff, and sewage, minimising overflow and other health hazards. Combined Sewer Overflow (CSO) events are a major challenge in UDS management, and many software techniques are used to reduce overflow events, for example Model Predictive Control (MPC). By applying an optimisation routine using a model of the UDS, MPC can reduce CSO events. However, for this method to be effective, it requires the selection of several parameters, which is typically done through trial-and-error. This paper proposes a methodology to identify the MPC’s parameters by using a Sensitivity Analysis (SA) combined with a Genetic Algorithm (GA). The methodology consists of identifying regions of effectiveness of the parameters for desired outputs using the SA and applying the knowledge into a GA which finds the best parameters. The results showed that SA reduced the computational cost for the GA while providing an excellent combination of parameters. The novel methodology is applied to a simple network with 3 CSO tanks, reducing the system’s total overflow over a 1-year period by 14% compared to when MPC is not implemented and 56% compared to a MPC tuned using trial-and-error.

Climate change, rapid urbanization, and environmental pressures require that Urban Drainage Systems (UDS) transition from a largely grey infrastructure to sustainable and adaptive systems. Real-Time Control (RTC) has emerged as a tool to enable adaptivity of the UDS to various pressures yet has not been explored as a tool to facilitate long-term transition by integrating within the urban water system. This paper presents a methodology for analysing transitioning scenarios and proposes a unifying RTC approach integrating the entire urban water system. A conceptual case of a Dutch municipality transitioning to a largely blue and green infrastructure (BGI)-based UDS illustrates the methodology's potential to align traditional goals, as flood and CSO reduction, with surface- and groundwater management. Results highlight the advantages of linking previously disconnected subsystems through an integrated control approach, accumulating benefits over time and space. The approach implies necessary changes in the traditional RTC modelling, as the boundaries of the systems expand and the RTC strategy itself has to be modified to include model coupling and data assimilation to address the complexity of integrated systems.

The LIFE RUBIES project aims to develop and deploy an operational tool in Lille (France) and Madrid (Spain) to reduce the impact of combined sewer overflows (CSO) during rain events, by controlling water pollution in real-time. Real-time control will enable decision making over the flow and storage of urban wastewater to prevent pollution, leveraging the coupling of sensors, models, and controllers.

This paper presents the work conducted in the Madrid case study to implement and test the system, improving the operation of part of its sanitation facilities (two stormwater tanks with a total capacity of 600.000 m³, two wastewater treatment plants, and the corresponding sewage network), and showcases some preliminary results.

The project started in October 2021 and will conclude in December 2025.

As illicit discharges may be highly transitory (< 1h), detecting, tracing, and eliminating them to improve stormwater health requires high frequency monitoring. This work presents a simple algorithm for image-based water depth monitoring and dry weather discharge alarms capable to be locally run on IoT cameras such as the BoSLcam. Local image analysis presents advantages over remote analysis including, greater robustness against poor cellular connectivity, lower cellular data usage and hence costs, and reduced battery usage.

Illicit discharges to the municipal storm drains degrade receiving water quality, compromise network flood control capacity, and threaten public health. Current monitoring paradigms are designed for detecting continuous illicit discharges, with known deficiencies for detecting transient discharges. This research describes a novel approach to automating detection of illicit discharges in storm drains. A pilot network of sensors was deployed to eight storm sewer outfalls in southern California. The sensor network continuously monitors key hydrologic and water quality variables for trends that indicate an illicit discharge. When a potential discharge is detected, an email alarm notifies stormwater managers in near real-time about the incident. Rapid responses enabled by automated detection greatly improves the likelihood that intermittent illicit discharges will be captured, traced, and eliminated. The open-source, 3D printed, IoT-enabled sensors utilized herein are a cost-effective alternative to products by commercial vendors, bringing full network coverage into a competitive price range.

Cape Town’s water challenges, including urban flooding and drought exacerbated by climate change and rapid urbanisation, demand innovative and scalable hydrological modelling solutions. This study presents an IoT-enabled hydrological modelling framework for stormwater management and Managed Aquifer Recharge (MAR) in Mitchells Plain, Cape Town, South Africa. Real-time data on groundwater levels, infiltration rates, and rainfall were collected using LoRaWAN-connected sensors and integrated into predictive hydrological models. Model calibration, sensitivity analysis, and forecasting techniques improved system reliability despite data challenges from vandalism and power interruptions. Results demonstrate the potential of IoT-enabled frameworks to support urban drainage modelling, climate resilience, and sustainable water management in resource-scarce environments.