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Updated: Oct 6, 2025

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

Khalid El Ghazouli1, Jamal El Khatabi2, Aziz Soulhi3

  • 1Laboratoire de Génie Civil et géo-Environnement, Univ. Lille, IMT Lille Douai, Univ. Artois, Yncrea Hauts-de-France, ULR, 4515 - LGCgE, Lille F-59000, France E-mail: elghazouli.khalid@gmail.com; Laboratoire d'Analyse des Systèmes, Traitement de l'Information et Management Industriel, Université Mohammed V, Rabat, Morocco.

Water Science and Technology : a Journal of the International Association on Water Pollution Research
|January 20, 2022
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Summary

This study introduces a novel Model Predictive Control (MPC) system using neural networks and genetic algorithms to optimize urban sewer systems. The approach effectively reduces combined sewer overflows (CSOs) and water pollution, even with limited urban drainage system capacity.

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Area of Science:

  • Environmental Engineering
  • Water Resource Management
  • Urban Planning

Background:

  • Urbanization and climate change increase precipitation intensity, overwhelming urban drainage systems (UDS) and causing combined sewer overflows (CSOs).
  • CSOs lead to significant water pollution in urban environments globally.
  • Existing UDS often lack the capacity to manage increased stormwater runoff effectively.

Purpose of the Study:

  • To develop and evaluate a novel Model Predictive Control (MPC) system for mitigating CSOs.
  • To optimize the operation of urban sewer systems for enhanced stormwater management.
  • To reduce the environmental impact of CSOs through intelligent control strategies.

Main Methods:

  • Implementation of a novel MPC framework integrating neural networks for flow prediction.
  • Utilization of a Stormwater Management Model (SWMM) for simulating flow conveyance.
  • Application of a genetic algorithm for optimizing sewer system operation and control strategies.
  • Testing the model on the sewer system of Casablanca, Morocco.

Main Results:

  • The developed MPC system demonstrated significant efficiency in reducing combined sewer overflows (CSOs).
  • The proposed model achieved short optimization times, facilitated by parallel computing.
  • Successful application and validation of the control strategy in a real-world urban sewer system.

Conclusions:

  • The novel MPC approach offers an effective solution for managing CSOs in urban areas.
  • Intelligent control strategies can significantly improve the performance of existing urban drainage systems.
  • The integration of predictive modeling and optimization algorithms provides a robust framework for sustainable urban water management.