Water quality-based real time control of integrated urban drainage: a preliminary study from Copenhagen, Denmark

AbstractGlobal Real Time Control (RTC) of urban drainage systems is increasingly seen as cost-effective solution for responding to increasing performance demands. This study investigated the potential for including water-quality based RTC into the global control strategy which is under implementation in the Lynetten catchment (Copenhagen, Denmark). Two different strategies were simulated, considering: (i) water quality at the wastewater treatment plant (WWTP) inlet and (ii) pollution discharge to the bathing areas. These strategies were included in the Dynamic Overflow Risk Assessment (DORA) RTC strategy, which allows for prioritization of the discharge points in the systems according to their sensitivity. A conceptual hydrological model was used to assess the performance of the integrated control strategy over an entire year. The simulation results showed the benefits of the proposed approaches in reducing Combined Sewer Overflow (CSO) loads at the WWTP inlet and in an upstream location discharging to sensitive bathing waters for medium CSO events (i.e. those with greater potential for control). Furthermore, when looking at the overall performance across the entire catchment during the simulation period, no significant changes were observed. These preliminary results require further analysis by including detailed water quality measurements and simulations. Nevertheless, the potential for including water-quality RTC in global RTC schemes was unveiled, providing a further option to urban water managers to improve the performance of their systems

A Cyber-Physical All-Hazard Risk Management Approach: The Case of the Wastewater Treatment Plant of Copenhagen

The ongoing digitalization of critical infrastructures enables more efficient processes, but also comes with new challenges related to potential cyber-physical attacks or incidents. To manage their associated risk, a precise and systematic framework should be adopted. This paper describes a general methodology that is consistent with the Risk Management ISO (31000-2018) and builds on specific tools developed within the H2020 digital-water.city (DWC) project. The approach has been demonstrated for a digital solution of the DWC project that allows to visualize inflow predictions for the Wastewater Treatment Plant (WWTP) in the city of Copenhagen. Specifically, the risk assessment and risk treatment steps are demonstrated in the case of the spoofing of the web interface where misleading forecast data may turn into fallacious maintenance schedules for the operators. The adopted methodology applied to the selected use case led to the identification of convenient measures for risk mitigation

Calibration and validation of a phenomenological influent pollutant disturbance scenario generator using full-scale data

The objective of this paper is to demonstrate the full-scale feasibility of the phenomenological dynamic influent pollutant disturbance scenario generator (DIPDSG) that was originally used to create the influent data of the International Water Association (IWA) Benchmark Simulation Model No. 2 (BSM2). In this study, the influent characteristics of two large Scandinavian treatment facilities are studied for a period of two years. A step-wise procedure based on adjusting the most sensitive parameters at different time scales is followed to calibrate/validate the DIPDSG model blocks for: 1) flow rate; 2) pollutants (carbon, nitrogen); 3) temperature; and, 4) transport. Simulation results show that the model successfully describes daily/weekly and seasonal variations and the effect of rainfall and snow melting on the influent flow rate, pollutant concentrations and temperature profiles. Furthermore, additional phenomena such as size and accumulation/flush of particulates of/in the upstream catchment and sewer system are incorporated in the simulated time series. Finally, this study is complemented with: 1) the generation of additional future scenarios showing the effects of different rainfall patterns (climate change) or influent biodegradability (process uncertainty) on the generated time series; 2) a demonstration of how to reduce the cost/workload of measuring campaigns by filling the gaps due to missing data in the influent profiles; and, 3) a critical discussion of the presented results balancing model structure/calibration procedure complexity and prediction capabilities