Model predictive control based on artificial intelligence and EPA-SWMM model to reduce CSOs impacts in sewer systems

Abstract

Urbanization and an increase in precipitation intensities due to climate change, in addition to limited urban drainage systems (UDS) capacity, are the main causes of combined sewer overflows (CSOs) that cause serious water pollution problems in many cities around the world. Model predictive control (MPC) systems offer a new approach to mitigate the impact of CSOs by generating optimal temporally and spatially varied dynamic control strategies of sewer system actuators. This paper presents a novel MPC based on neural networks for predicting flows, a stormwater management model (SWMM) for flow conveyance, and a genetic algorithm for optimizing the operation of sewer systems and defining the best control strategies. The proposed model was tested on the sewer system of the city of Casablanca in Morocco. The results have shown the efficiency of the developed MPC to reduce CSOs while considering short optimization time thanks to parallel computing. HIGHLIGHTS Model predictive control of smart sewer networks.; Artificial Neural Networks and parallel computing enhance the proactivity of the MPC.; Real-Time Control.; Combined sewer overflows reduction.;

Keywords

• artificial intelligence
• combined sewer overflows
• genetic algorithm
• model predictive control
• real-time control
• sewer network