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Sanitary sewer systems are critical urban water infrastructure that protect both human and environmental health. Their design, operation, and monitoring require novel modeling techniques that capture dominant processes while allowing for computationally efficient simulations. Open water flow in sewers and rivers are intrinsically similar processes. With this in mind, we formulated a new parsimonious model inspired by the Width Function Instantaneous Unit Hydrograph (WFIUH) approach, widely used to predict rainfallrunoff relationships in watersheds, to a sanitary sewer system consisting of nearly 10,000 sewer conduits and 120,000 residential and commercial sewage connections in Northern Virginia, U.S.A. Model predictions for the three primary components of sanitary flow, including Base Wastewater Flow (BWF), Groundwater Infiltration (GWI), and Runoff Derived Infiltration and Inflow (RDII), compare favorably with the more computationally demanding industry-standard Storm Water Management Model (SWMM). This novel application of the WFIUH modeling framework should support a number of critical water quality endpoints, including (i) sewer hydrograph separation through the quantification of BWF, GWI, and RDII outflows, (ii) evaluation of the impact of new urban developments on sewage flow dynamics, (iii) monitoring and mitigation of sanitary sewer overflows, and (iv) design and interpretation of wastewater surveillance studies.
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Optimal Control of Combined Sewer Systems to Minimize Sewer Overflows by Using Reinforcement Learning
A combined sewer system (CSS) collects rainwater runoff, domestic sewage, and industrial wastewater in the same pipe. The volume of wastewater can sometimes exceed the system capacity during heavy rainfall events. When this occurs, untreated stormwater and wastewater discharge directly to nearby streams, rivers, and other water bodies. This would threaten public health and the environment, contributing to drinking water contamination and other concerns. Minimizing sewer overflows requires an optimization method that can provide an optimal sequence of decision variables at control gates. Conventional strategies use classical optimization algorithms, such as genetic algorithms and pattern search, to find the optimal sequence of decision variables. However, these conventional frameworks are very time-consuming, and it is almost impossible to achieve near real-time optimal control. This paper presents a faster optimization framework by using a new optimal control tool: reinforcement learning. The environment (flow modeler) used in this paper is the numerical model: Environmental Protection Agency’s Storm Water Management Model (EPA SWMM) to ensure the accuracy of environment response. The reward function is constructed based on the calculated water depth and overflow rate from SWMM. The process keeps minimizing the reward function to obtain the optimal flow release sequence at each controlled orifice gate. The combined sewer system (CSS) of the Puritan-Fenkell 7-mile facility in Detroit, MI, is chosen as the case study.
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- Award ID(s):
- 2203292
- PAR ID:
- 10459969
- Date Published:
- Journal Name:
- World Environmental and Water Resources Congress 2023
- Page Range / eLocation ID:
- 711 to 722
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1061/9780784484852.067
