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.

Advances in cyber–physical technologies have enabled real-time sensing and adaptive control of stormwater infrastructure. These smart stormwater systems allow for inexpensive, minimally-invasive stormwater control interventions in lieu of new construction. However promising the area of smart stormwater control, there still remain barriers – for experts and novices alike – to access shared tools and methods for investigating, developing, and contributing to it. In an effort to make smart stormwater control research more methodical and accessible, we present pystorms, an open-source Python-based simulation sandbox that facilitates the quantitative evaluation and comparison of control strategies. pystorms consists of a collection of real world-inspired smart stormwater control scenarios on which any number of control strategies can be applied and tested via an accompanying Python programming interface and coupled stormwater simulator. pystorms provides a framework for the rigorous and efficient evaluation of smart stormwater control methodologies across diverse watersheds with only a few lines of code.

Stormwater management seeks to reduce runoff from rain or melted snow and improve water quality.Where it can absorb into soil, runoff is filtered and returns to streams, rivers, and aquifers, but in developed areas, precipitation often cannot soak into the ground because impervious surfaces (e.g., pavement, buildings), and already saturated soils can create excess runoff.This water, which can contain pollutants, then runs across urban surfaces and into storm drains, drainage ditches, and sewer systems.Stormwater runoff can cause flooding, erosion, infrastructure and habitat damage, and contamination (including combined and sanitary sewer overflows).In urban and developed areas, effective stormwater management that routes and detains stormwater helps to mitigate these impacts and improve water quality.

Settling solids upstream of a combined sewer overflow (CSO) have led to an undesirable odour issue in warm temperatures and elevated environmental pollutant loading during the first flush period of wet weather events.Several strategies exist to ameliorate the solids discharged during the first flush period of an overflow event, with one strategy being the use of a dry weather channel (DWC).A DWC is a collection system design feature that can be used to limit and reduce solids deposition within the collection system, by maintaining higher forward flow velocities during low flow while reducing the settleable surface area within the collection system for solids accumulation.This paper describes how we employed a first order solids transport model from Willems (2009) to represent the settling and washoff rates within the collection system in conjunction with the P8 urban catchment model from Walker (1990) to model the influent mass rate from overland flow.The model was subsequently refined to incorporate the concept of uniform settling on the wetted surfaces within the collection system.When comparing the existing system to the proposed system, modeling results at the CSO outflow point suggest that a DWC could reduce the solids discharged from the CSO by approximately 25% annually.

Trap grease is an environmental burden and its management has been costly and ineffective. Utilizing trap grease as a feedstock for biodiesel has the potential to reduce the cost of waste removal and biofuel production. This study presents a life cycle analysis to evaluate the energy consumption and greenhouse gas (GHG) emission from the trap grease-to-biodiesel production process. It was shown that utilizing the solids in the trap grease for anaerobic digestion (AD) was crucial in reducing both energy consumption and GHG emissions. Monte Carlo simulation revealed significant variation in both the life cycle energy consumption and GHG emission, which was caused by the uncertainties within several key variables. The result of the sensitivity analysis indicated that trap grease has the potential to be a more energy efficient and low-GHG-emission feedstock under certain conditions, as compared with the current common feedstocks (e.g. soybean and algae).