Nonlinear Grey-Box Identification with Inflow Decoupling in Gravity Sewers

Knowing where wastewater is flowing in drainage networks is essential to utilize system storage, predict overflows and to optimize system operation. Unfortunately, flow in gravity-driven sewers is subject to transport delays, and typically influenced by significant disturbances entering the sewer pipes in the form of domestic, ground and rain inflows. Model-based optimal control of urban drainage requires knowledge about these inflows, even though it is often not feasible in operational setups. To this end, we propose a lumped-parameter hydrodynamic model with a bi-linear structure for identifying the transport delays, decouple periodic disturbances and to predict the discharged flow. Pumped inlet and discharged dry- weather flow is used to find the model parameters. Under mild assumptions on the domestic and groundwater inflows, i.e. disturbances, the decoupling capabilities of the identified model are presented. A numerical case study on an EPA Storm Water Management Model (EPA SWMM) and experimental results on a real network demonstrate the proposed method.