STTR Phase I: Machine learning and video-based sensor for measuring sewer flows

Award ID: 2151637

The broader impact of this Small Business Technology Transfer (STTR) Phase I project will be to provide water reclamation facilities with accurate and reliable data across all pipe flow conditions in order to make infrastructure and operational decisions. This decision-making capability is important as many facilities spend millions of dollars each year on improving sewer system function, yet are constrained by lack of quality data on sewer flows. Improved sewer flow data may help to optimize infrastructure improvements and reduce costs to taxpayers. In addition, this work may directly advance the health and welfare of the American public through improved wastewater collection systems operations and reduced overflows and basement backups. This technology has broader social implications as low-income and minority communities are disproportionately affected by flood impacts. This Small Business Technology Transfer (STTR) Phase I project seeks to advance a novel, non-contact sensor that collects video of wastewater flow in sanitary sewer systems to measure flow rate and detect critical sewer events. Specifically, the technology collects video of sanitary sewer flows and processes it in real time using a machine learning algorithm to measure the velocity and water level of the flow. This technology also evaluates images of sewer flows to identify illicit discharges into the system. Several key technical hurdles crucial to successful commercialization of this innovation will be addressed in the proposed project, including the use of artificial illumination systems in closed pipe environments, the development of strategies to account for rapid variations in flow rates, and the development of data analytic methods to identify critical sewer events. Given these technical hurdles, the objectives of this project will be to develop a video-based flow sensor that can accurately capture velocity and water level under expected environmental conditions and to analyze data from the proposed sensor to accurately identify critical sewer events such as blockages and illicit discharges. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Peter Atherton