Intelligent control of combined sewer systems using PySWMM-A Python wrapper for EPA's Stormwater Management Model

Area of Science:

• Environmental Engineering
• Water Resource Management
• Computational Hydraulics

Background:

• Wastewater utilities balance public health and infrastructure maintenance under budget constraints.
• Small to medium-sized utilities often face significant financial limitations.
• Aging infrastructure necessitates cost-effective solutions beyond traditional upgrades.

Purpose of the Study:

• To introduce PySWMM, an open-source Python wrapper for the EPA Storm Water Management Model (SWMM).
• To enable advanced real-time control strategies for intelligent water systems.
• To demonstrate the application of PySWMM in mitigating combined sewer overflows.

Main Methods:

• Developed PySWMM as a Python interface for the SWMM computational engine.
• Facilitated runtime interaction to modify SWMM parameters and control simulations.
• Integrated SWMM with Python's scientific computing, big data, and machine learning libraries.
• Applied PySWMM in real-world case studies in Cincinnati and Columbus, Ohio.

Main Results:

• PySWMM allows flexible runtime control and parameter modification of SWMM simulations.
• The tool enables the integration of advanced algorithms with established hydrologic and hydraulic models.
• Case studies demonstrated significant reductions in combined sewer overflows.

Conclusions:

• PySWMM offers a powerful, open-source solution for developing sophisticated intelligent water control strategies.
• The integration of PySWMM with Python's ecosystem facilitates data-driven optimization of wastewater systems.
• Intelligent water approaches, enabled by tools like PySWMM, are effective in addressing combined sewer overflows and improving water quality.