Tryby M E, Buahin C A, McDonnell B E, Knight W J, Fortin-Flefil J, VanDoren M, Eckenwiler S, Boyer H
United States Environmental Protection Agency, Office of Research and Development, Andrew W. Breidenbach Environmental Research Center, Cincinnati, 45268, OH, USA.
HydroDigital LLC, PO Box 1243, South Bend, 46624, IN, USA.
Environ Model Softw. 2024 Nov 26;19. doi: 10.1016/j.envsoft.2024.106114.
Wastewater utilities face competing priorities as they work to protect human health and water quality, and to maintain infrastructure in their communities. Budgetary constraints can be especially pronounced among small to medium-sized utilities. Utilities are increasingly turning to so-called intelligent water approaches as a cost-effective alternative to upgrading aging infrastructure. Intelligent water encompasses automated control and real-time decision support technologies and can be applied at scale to large and small utilities alike accommodating differences in needs, capabilities, and funds. Intelligent water upgrades can be designed to optimize existing conveyance, storage, and treatment during storms to help mitigate flooding and combined sewer overflows. The most promising real-time control algorithms coordinate control of upstream and downstream assets and are designed using urban hydrologic and hydraulic modeling software. The capabilities of legacy software, however, can sometimes inhibit the creation of sophisticated control algorithms. In this paper, we present PySWMM - an open-source Python wrapper developed for the EPA Storm Water Management Model (SWMM). PySWMM enables runtime interactions with the SWMM computational engine to flexibly read, modify system parameters, and control digital infrastructure during a simulation. Crucially, it allows modelers to easily combine SWMM with the rich set of scientific computing, big data, and machine learning modules found in the Python ecosystem. We highlight two real-world intelligent water case studies utilizing PySWMM in the cities of Cincinnati and Columbus, Ohio where it has helped to eliminate tens of millions of gallons of combined sewer overflows annually.
废水处理部门在努力保护人类健康和水质以及维护社区基础设施时面临着相互竞争的优先事项。预算限制在中小型处理部门中可能尤为突出。处理部门越来越多地转向所谓的智能水方法，作为升级老化基础设施的一种经济有效的替代方案。智能水涵盖自动化控制和实时决策支持技术，并且可以大规模应用于大小处理部门，以适应需求、能力和资金方面的差异。智能水升级设计可在暴雨期间优化现有的输送、储存和处理，以帮助减轻洪水和合流制下水道溢流。最有前景的实时控制算法可协调上游和下游资产的控制，并使用城市水文和水力建模软件进行设计。然而，传统软件的功能有时会阻碍复杂控制算法的创建。在本文中，我们介绍了PySWMM——为美国环境保护局雨水管理模型（SWMM）开发的开源Python包装器。PySWMM允许在运行时与SWMM计算引擎进行交互，以便在模拟过程中灵活读取、修改系统参数并控制数字基础设施。至关重要的是，它允许建模人员轻松地将SWMM与Python生态系统中丰富的科学计算、大数据和机器学习模块结合起来。我们重点介绍了在俄亥俄州辛辛那提市和哥伦布市利用PySWMM进行的两个实际智能水案例研究，在那里它每年帮助消除数千万加仑的合流制下水道溢流。
