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.

该小型企业技术转移（STTR）I阶段项目的更广泛影响是在所有管道流动条件下为水上填海设施提供准确可靠的数据，以做出基础架构和运营决策。这种决策能力很重要，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，但由于缺乏下水道流量的质量数据而受到限制。改善的下水道流量数据可能有助于优化基础设施的改进并降低纳税人的成本。此外，这项工作可能直接通过改进的废水收集系统操作以及减少溢出和地下室备份来直接推动美国公众的健康和福利。这项技术具有更广泛的社会影响，因为低收入和少数群体受洪水影响的影响不成比例。本小型企业技术转移（STTR）I阶段项目旨在推进一种新型的非接触传感器，该传感器收集了卫生下水道系统中废水流量的视频，以衡量流量和检测重要的下水道事件。具体而言，该技术收集了卫生下水道流动的视频，并使用机器学习算法实时处理它，以测量流量的速度和水位。该技术还评估了下水道流的图像，以识别系统的非法排放。拟议项目将解决这一创新成功商业化至关重要的几个关键技术障碍，包括在封闭的管道环境中使用人工照明系统，发展流速快速变化的策略以及数据分析方法的发展以识别关键的下水道事件。鉴于这些技术障碍，该项目的目标将是开发基于视频的流动传感器，该传感器可以在预期的环境条件下准确地捕获速度和水位，并分析来自拟议传感器的数据，以准确地确定诸如封锁和非法排放之类的关键下水道事件。该奖项颁发了NSF的法定任务，并通过评估范围来反映了概念的支持者，并已被评估概念范围。