In this work, a control-oriented sewer network model is presented based on a hybrid linear modeling framework. The model equations are described independently for each network element, thus allowing the model to be applied to a broad class of networks. A parameter calibration procedure using data obtained from simulation software that solves the physically based model equations is described and validation results are given for a case study. Using the control model equations, an optimal control problem to minimize flooding and pollution is formulated to be solved by means of mixed-integer linear or quadratic programming. A receding horizon control strategy based on this optimal control problem is applied to the case study using the simulation software as a virtual reality. Results of this closed-loop simulation tests show the effectiveness of the proposed approach in fulfilling the control objectives while complying with physical and operational constraints.
An output‐feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model‐based predictive control to large‐scale sewer networks, in‐spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control‐oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solving these problems, preliminary Receding Horizon Control with Moving Horizon Estimation (RHC/MHE) results, based on flow measurements, were also obtained. In this work, the RHC/MHE algorithm has been extended to take into account both flow and water level measurements and the resulting control loop has been extensively simulated to assess the system performance according different measurement availability scenarios and rain events. All simulations have been carried out using a detailed physically based model of a real case‐study network as virtual reality.
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