Real-time control of urban drainage networks is a complex task where transport flows are non-pressurized and therefore impose flow-dependent time delays in the system. Unfortunately, the installation of flow sensors is economically out of reach at most utilities, although knowing volumes and flows are essential to optimize system operation. In this article, we formulate joint parameter and state estimation based on level sensors deployed inside manholes and basins in the network. We describe the flow dynamics on the main pipelines by the level variations inside manholes, characterized by a system of coupled partial differential equations. These dynamics are approximated with kinematic waves where the network model is established with the water levels being the system states. Moving horizon estimation is developed where the states and parameters are obtained via the levels and estimated flow data, utilizing the topological layout of the network. The obtained model complexity is kept within practically achievable limits, suitable for nonlinear predictive control. The effectiveness of the control and estimation method is demonstrated on a high-fidelity model of a drainage network, acting as virtual reality. We use real rain and wastewater flow data and test the controller against the uncertainty in the disturbance forecasts.

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Urban drainage networks (UDN) are among the most vital infrastructures within the natural water cycle. The most widely applied Real Time Control (RTC) on these systems is Model Predictive Control (MPC), which typically incorporates transport time delays and the effect of disturbances explicitly in the objectives and constraints. One of the greatest challenges in the control of UDNs is to formulate multiple control criteria regarding operational requirements of the network. Furthermore, MPC faces the challenge of handling uncertainty caused by disturbances, e.g. weather predictions.One way to incorporate the uncertainty in the decision making is to consider multiple scenarios, i.e. to generate different ensembles based on rain forecasts. To this end, we propose a Multi-scenario MPC (MS-MPC) approach, that deals with uncertainty in the expected inflow. First, a generic multiobjective MPC is established which deals with the time delays explicitly in the optimization. Then, this framework is extended to our formulation of the multiple scenario problem. The algorithm is verified through a case study by interfacing a highfidelity simulator model of a sewer network as virtual reality.