CSO Reduction by Integrated Model Predictive Control of Stormwater Inflows: A Simulated Proof of Concept Using Linear Surrogate Models. Issue 8 (20th August 2020) - Lund, N. S. V.; Borup, M.; Madsen, H.; Mark, O.; Mikkelsen, P. S.
Abstract: Combined sewer overflows (CSO) of mixed stormwater and wastewater pollute nearby receiving surface waters and pose a risk to the environment and human health. We use "integrated stormwater inflow control" to mitigate CSO by dynamically controlling the inflow of stormwater to the combined sewer system in real time, expanding the physical space of traditional real‐time control. This control is carried out with model predictive control (MPC), which we base on convex optimization including a linear internal surrogate model of the controllable aboveground and belowground infrastructure. A detailed hydrodynamic model is used to evaluate the results and recursively initialize the surrogate model. MPC dynamically decides when to let stormwater enter the sewer system and when to store and convey excess stormwater in the aboveground infrastructure otherwise intended for passive cloudburst management. The performance was quantified in a simulation study in Copenhagen, Denmark, using a 1‐D distributed hydrodynamic model and 32 rain events from 2016, of which 18 caused CSO in the situation without control. Four of the 18 CSO events were avoided with MPC, and the total CSO volume was reduced by 98.4% of the potential reducible volume. For one event, stormwater was unnecessarily kept on the surface because the surrogate model wrongly predicted a CSO. The computational cost was in all cases compatible with an operational implementation. With the invention of proper actuators for control of stormwater inflows, we show that MPC of stormwater inflows may be a viable supplement to more traditional passive ways of managing stormwater in urban areas. Key Points: Integrated model predictive control of stormwater inflows can reduce overflows, almost as much as disconnecting stormwater from the sewers Linearized surrogate models and convex optimization make implementation compatible with the time constraints in an operational setting The performance depends on the prediction horizon and the recursive initialization of the surrogate models with a detailed model