Model Predictive Control with SWMM
Real time control (RTC) is particularly promising in large, flat and heterogeneous sewer systems with a high in-line storage volume. For the simulation of such systems with major backwater effects the use of dynamic routing models is indicated, but for model predictive control (MPC) such models are generally regarded as infeasible because they are computationally highly demanding and thus impractical to use for receding horizon applications.
This chapter focuses on the challenges and constraints of dynamic flow routing calculations for MPC. For the analysis a software framework was developed which enables MPC simulations using the dynamic sewer network model SWMM 5 (Rossman, 2008). The software provides various optimization algorithms and offers different time horizons to take into ac-count the time span required to evaluate the optimization objectives (prediction horizon), the time span for which system input is known in advance (forecast horizon), and the time span for which control devices have to be optimized (control horizon). For the formulation of control objectives, parameters representing flow and water quality conditions can be used. In the generated MPC framework, modules for optimization and flow simulation are separate, leading to a text-based parameter optimization procedure.
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