Conference Agenda

Flow-dividing structures in combined sewer systems serve to mitigate hydraulic overloads during storm events by providing retention capacity. Their operational performance can be enhanced by adjusting the continuation flows regulated by their flow control devices. To optimise system efficiency to reduce emissions, it is essential to use existing volumetric capacities more efficiently, e.g. by adjusting continuation flows. To assess this potential, statistical analyses are conducted on water level measurements from combined sewer overflow (CSO) structures (e.g. frequency analyses) to derive meta information used to assess the baseline functioning. Meta information, such as weir heights, basin base areas and continuation flows, with higher accuracy than would be feasible through manual measurements in the structures or extraction from construction plans, are derived solely from measured water levels. Derived meta information is employed to isolate events to determine reliably CSO occurrence and duration. The continuation flows are optimised using an emulator coupled with a genetic algorithm, with the objective of achieving enhanced operational performance. The case study, which involved eight CSO structures, revealed improvements in efficiency. Overall system efficiency increased from the baseline functioning to the optimized operational setup through the static adjustment of the continuation flows within a range of +/- 50%.

Despite water regulations and standards that often favor separate sewer networks over combined ones, the substantial investments required-along with financial and economic limitations-make the selection of urban drainage systems particularly challenging for city managers, especially in developing countries. This study aims to identify the most suitable system for hot and dry regions by examining a real-world case in Ahvaz, Iran. Using Ziggurat.ai-an automated platform for designing and optimizing drainage systems-both combined and separate networks were planned with a focus on minimizing construction costs. Their performance under extreme loading conditions was then evaluated using SWMM software. Subsequently, a hierarchical framework of criteria and sub-criteria was established, and pairwise comparison matrices were developed based on modeling results and expert judgments. The final analysis reveals that the combined sewer network, is a better urban drainage solution for the given test case.

Combined Sewer Overflows (CSOs) release untreated wastewater during heavy rain, but their impact on water quality and even their magnitude is poorly understood due to limited monitoring. This study develops a prototype of an Agent-Based Model (ABM) to test policies for increasing sensor adoption in CSO monitoring in Switzerland. The ABM simulates the behaviour of key stakeholders, including municipalities, wastewater treatment plant operators, engineers, and cantonal authorities, using the Theory of Planned Behaviour and the Bounded Confidence Model. It evaluates three strategies: professional events, mandatory sensor installation, and improved sensor technology. Results suggest mandatory installation is the most effective, though findings should be interpreted cautiously due to limited data. The modelling process improved our understanding of the socio-technical system and highlighted the role of social dynamics in technology adoption. Our model integrates multiple knowledge sources, includes a social network structure, and is openly accessible for further development. Key limitations include simplified human behaviour and static assumptions, but ABMs remain valuable to test policies and analyse complex systems.

Automatic process control is a proven method for enhancing safety, productivity, and cutting operational costs across different industries. While some sectors have embraced advanced controllers, others are just starting to implement closed-loop systems. Urban sanitation systems often rely on basic on-off controllers, despite the critical role they play in urban water management. Poor control can lead to combined sewer overflows. In our study, we maintain controller simplicity by using finite state machines but introduce two key enhancements: a centralized control strategy that monitors the entire tank system simultaneously and a fine-tuned strategy through optimization to minimize system overflows objectively. Testing the new controller in a simulated urban setting, considering effluent flow rates, quality variations, and rainfall changes, we achieved a significant 56% reduction overflow compared to the initial control proposal.

Urban runoff is expected to rise as a result of urbanization and the intensification of the hydrological cycle caused by climate change, which could lead to more frequent wastewater spills through combined sewer overflows (CSOs). To this end, cities will need new methodologies and tools that enable the planning of nature-based solutions for urban runoff management towards resilient and sustainable management of urban drainage systems.

In our contribution, we intend to demonstrate our block-level, reduce-data method for managing urban runoff. This method is provided as a modular tool framework comprising of various tools to ease the workflow for setting up urban drainage models. This contribution will focus on the tool’s core functionality, which integrates the spatial discretization of urban blocks with the hydraulic sewer network model, presenting a simplified and innovative approach to managing urban water challenges. Until now, the urban block mapping tool for subcatchment delineation and planning of Low Impact Development has been applied in catchments in France and Germany. As for the sewer network modelling component of the tool, we have developed a Python package for generating synthetic sewer network layouts called pysewer.