Veolia (Denmark)

Wastewater‐based epidemiology (WBE) has been proven to be a useful tool in monitoring public health‐related issues such as drug use, and disease. By sampling wastewater and applying WBE methods, wastewater‐detectable pathogens such as viruses can be cheaply and effectively monitored, tracking people who might be missed or under‐represented in traditional disease surveillance. There is a gap in current knowledge in combining hydraulic modeling with WBE. Recent literature has also identified a gap in combining machine learning with WBE for the detection of viral outbreaks. In this study, we loosely coupled a physically‐based hydraulic model of pathogen introduction and transport with a machine learning model to track and trace the source of a pathogen within a sewer network and to evaluate its usefulness under various conditions. The methodology developed was applied to a hypothetical sewer network for the rapid detection of disease hotspots of the disease caused by the SARS‐CoV‐2 virus. Results showed that the machine learning model's ability to recognize hotspots is promising, but requires a high time‐resolution of monitoring data and is highly sensitive to the sewer system's physical layout and properties such as flow velocity, the pathogen sampling procedure, and the model's boundary conditions. The methodology proposed and developed in this paper opens new possibilities for WBE, suggesting a rapid back‐tracing of human‐excreted biomarkers based on only sampling at the outlet or other key points, but would require high‐frequency, contaminant‐specific sensor systems that are not available currently.

A wide variety of technologies addressing nutrient removal in wastewater treatment have been developed in the latest years. In order to be able to design, manage, operate, optimise and benchmark these novel technologies with potentially existing standard technologies, detailed modelling of nutrient removal becomes essential. Nutrient removal is prominently considered, with different degrees of complexity, in the commonly used Activated Sludge Models (ASM). However, the description of nutrient-related compounds in these standard models is not sufficiently detailed for novel and not so novel technologies. Furthermore, the environmental evaluation of a process, which includes an estimation of the greenhouse gas release, would require a more complex process to represent nitrogen removal. This chapter reviews the current state of nutrient removal modelling, with special focus on the needs created by emerging technologies in nitrogen and phosphorus removal, paying special attention to the objectives that must be evaluated for each technology.

\n\t\t\t\tGlobal Real Time Control (RTC) of urban drainage system is increasingly seen as cost-effective solution in order to respond to increasing performance demand (e.g. reduction of Combined Sewer Overflow, protection of sensitive areas as bathing water etc.). The Dynamic Overflow Risk Assessment (DORA) strategy was developed to operate Urban Drainage Systems (UDS) in order to minimize the expected overflow risk by considering the water volume presently stored in the drainage network, the expected runoff volume based on a 2-hours radar forecast model and an estimated uncertainty of the runoff forecast. However, such temporal horizon (1-2 hours) is relatively short when used for the operation of large storage facilities, which may require a few days to be emptied. This limits the performance of the optimization and control in reducing combined sewer overflow and in preparing for possible flooding. Based on DORA's approach, this study investigated the implementation of long forecast horizon using an ensemble forecast from a Numerical Weather Prediction (NWP) model. The uncertainty of the prediction is characterized by an ensemble of 25 forecast scenarios. According to the status of the UDS and the forecasted runoff volumes, the objectives for the control strategies might vary from optimization of water volumes to reduction of CSO risk. Thus different modes are implemented in DORA-LF (Long Forecast) in order to adjust the control strategies to the situations. In order to handle the long forecast, the horizon is divided into multiple and variable time step. This new approach was tested on selected rain events and shows an improvement in the protection of sensitive areas during long or/and coupled events by allowing anticipated CSO in low sensitivity areas.\n\t\t\t

Forecast-based flow prediction in drainage systems can be used to implement real-time control of drainage systems. This study compares two different types of rainfall forecast - a radar rainfall extrapolation-based nowcast model and a numerical weather prediction model. The models are applied as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real-time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 h. The best performance of the system is found using the radar nowcast for the short lead times and the weather model for larger lead times.