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

STTR 第一阶段：用于测量下水道流量的机器学习和基于视频的传感器

• 批准号: 2151637
• 负责人: Spencer Sebo
• 金额: $ 25.6万
• 依托单位: WATER INTELLIGENCE LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2151637&HistoricalAwards=false
• 关键词: STTR; Phase; Machine; learning; video

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.

小企业技术转让（STTR）第一阶段项目的更广泛影响将是为水回收设施提供所有管流条件下准确可靠的数据，以便做出基础设施和运营决策。这种决策能力非常重要，因为许多设施每年花费数百万美元用于改善下水道系统功能，但由于缺乏关于下水道流量的高质量数据而受到限制。改进的下水道流量数据可能有助于优化基础设施的改善，并降低纳税人的成本。此外，这项工作可以通过改善废水收集系统的运作和减少溢流和地下室备份，直接促进美国公众的健康和福利。这项技术具有更广泛的社会意义，因为低收入和少数民族社区受到洪水影响的比例不成比例。这个小企业技术转让（STTR）第一阶段项目旨在推进一种新型的非接触式传感器，它可以收集污水下水道系统中废水流量的视频，以测量流量并检测关键的下水道事件。具体来说，该技术收集污水管道流量的视频，并使用机器学习算法对其进行真实的实时处理，以测量流量的速度和水位。这项技术还可以评估下水道流量的图像，以识别进入系统的非法排放。几个关键的技术障碍，这一创新的成功商业化将在拟议的项目中解决，包括使用人工照明系统在封闭的管道环境中，战略的发展，以占流速的快速变化，以及数据分析方法的发展，以确定关键的下水道事件。鉴于这些技术障碍，该项目的目标是制作一个视频-该奖项反映了NSF的法定使命，并通过使用基金会的知识产权进行评估，被认为是值得支持的。优点和更广泛的影响审查标准。