RTC strategy reducing the ecological impact of an urban drainage system

Abstract

Urban wastewater systems can impact the urban ecology by untreated wastewater discharges through combined sewer overflow (CSO) events, or by partial treatment of the wastewater at the wastewater treatment plant (WWTP). CSO events can cause oxygen depletion, eutrophication, and the discharge of pathogens. The partial treatment of the wastewater causes an increased concentration of ammonium in the WWTP effluent, which can lead to toxic ammonium levels in the receiving river water. These problems can be (partially) mitigated by optimizing the existing infrastructure. Optimization of the available storage will help handle the increased pressure on the urban drainage system (UDS) and the stricter environmental regulations simultaneously. A method to control the dynamic performance of the combined sewer system is Real-Time Control (RTC). A RTC strategy controls the combined sewer system dynamically based on real-time information about the system state. This research aims to develop a RTC strategy that decreases the negative ecological impact of the combined sewer system on the river by optimizing the available in-sewer volume. By doing so, the objectives to reduce the total amount of spilled CSO volume and to decrease the ammonium peaks towards the WWTP should be met. This research is applied to the case study of Geldrop-Mierlo, this is a municipality located in the UDS of Eindhoven. The trade-off between those two objectives was explored in the Wastewater Process simulator WEST. Rainfall events with a maximum intensity of 3.1 mm/hr and higher or rainfall events with maximum intensity < 3 mm/hr and total rainfall depth of > 4.8 mm, were found to be more likely to cause DO dips. The objective function which is used in the optimization process is dependent on the forecasted rainfall and the trade-off described above. The UDS is modeled in a full-hydrodynamic (FH) model and a simplified conceptual model. The conceptual model is made to reduce the computation time. The catchment of the FH model is split up into 3 different catchments, and each is modeled as a reservoir in the conceptual model. The characteristics of each reservoir are dependent on the characteristics of these catchments in the FH model. The characteristics that are included are storage curve, outflow dynamics, and CSO dynamics. Both models are calibrated and validated. The UDS is controlled with the Model Predictive Control (MPC) methodology using a Genetic Algorithm (GA) to find the optimal solution to minimize the negative ecological impact of the UDS on the river. Both the FH model and simplified model are used in the MPC optimization. Based on the analysis of the case study, the optimization results show that the impact of the MPC procedure on the receiving river is not significant. The reasons for this are location specific, but the main findings are that 1) the hydraulic constraints of the catchments restrict the MPC procedure from working, 2) although the calibration results of the conceptual model indicated accurate results, this does not guarantee that the model is also accurate enough to use in the MPC procedure.