Sanitation networks are vital infrastructure in modern society. They are used for transporting wastewater and rainwater from cities to treatment facilities, where wastewater is treated before being released into the environment. Most countries still use combined sanitation networks where wastewater and rainwater are transported in a single pipe. This leaves the combined sanitation network prone to overflow in the event of heavy rainfall. A solution to minimizing the overflow is Real-Time Control (RTC). A popular state-of-the-art RTC method used to anticipate and minimize overflows is standard Model Predictive Control (MPC). However, the standard formulation of the MPC does face challenges when dealing with the uncertainty caused by the inflow disturbances, i.e., the weather forecasts. To better handle the uncertainties, we propose an extended model predictive framework called Chance-Constrained MPC (CC-MPC). First, the nominal multiobjective MPC is formulated to deal with the challenges in the sanitation network. Then, the framework is extended to our stochastic MPC formulation. Two controllers are compared in a laboratory emulation of the network subsystem that we call the Two Tank Topology. Gravity pipe elements determine the primary dynamics that define the transport of wastewater through a network. Both controller frameworks require a model that can capture gravity pipe dynamics to predict overflow. Therefore, we developed a linear Diffusion Wave model based on the discretized Saint-Venant partial differential equations. The model is validated through a data-driven parameter estimation framework. Identification is conducted in a real network simulation and the real-life experimental setup created in AAU Smart Water Lab.
|Effective start/end date||01/09/2020 → 03/06/2021|
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):