CAREER: Towards Watersheds as Water Treatment Plants through Advances in Distributed Sensing and Control

职业：通过分布式传感和控制的进步将流域打造为水处理厂

• 批准号: 2340176
• 负责人: Matthew Bartos
• 金额: $ 51.91万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-09-01 至 2029-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340176&HistoricalAwards=false
• 关键词: CAREER; Towards; Watersheds; Water; Treatment

This Faculty Early Career Development (CAREER) award supports research that will explore the potential for real-time control of hydraulic infrastructure to reduce nutrient pollution in river networks. Nutrient pollution is one of the most costly and widespread environmental problems facing surface water systems worldwide. Nitrogen and phosphorus from agricultural and urban runoff fuel the growth of algae in rivers, lakes, and coastal waters. These algal blooms deplete oxygen levels and release toxins that are hazardous to human health. This project will investigate how real-time control of hydraulic infrastructure like dams can treat nutrient pollution by emulating processes from wastewater treatment plants at the watershed scale. This new approach has the potential to substantially reduce nutrient pollution impacts while obviating the need for expensive infrastructure expansion projects. These research efforts will be complemented with an educational plan designed to promote active learning of surface water quality concepts through the development of a new computer simulation game targeted at students of all levels. The research activities will be well integrated into teaching and outreach plans, to further disseminate the impacts through active and interactive learning opportunities to broaden participation in science, technology, engineering, and mathematics (STEM). This project will reveal new fundamental knowledge about the effects of hydraulic controls on nutrient dynamics using a combination of computational modeling and real-world experimental assessments. First, a new computational water quality model will be developed to simulate nutrient fate and transport in river networks accounting for unsteady hydraulics, multispecies reaction kinetics, and hyporheic exchange. Next, new methods for water quality data assimilation will be investigated to provide real-time estimates of water quality required for effective control. Third, optimal reservoir operation strategies for nutrient and algae removal will be explored using a robust model-predictive control approach and evaluated at the river network scale. Modeling, estimation, and control of nutrient dynamics will be validated against measured data using both a laboratory-scale testbed and system-scale case studies focused on real-world watersheds of different sizes. This research will characterize and quantify the potential for active control of reservoirs to mitigate nutrient pollution and algal blooms at the watershed scale. To facilitate broad educational outreach, the results of this research will be integrated into a new ‘sandbox’ computer game that will simulate nutrient-ecosystem interactions in river networks. By encouraging students to steward aquatic life in their own virtual ecosystems, this simulation game will organically teach the mechanics of eutrophication and the interventions needed to prevent it.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.

该学院早期职业发展（CAREER）奖支持研究，将探索实时控制水力基础设施的潜力，以减少河流网络中的营养物污染。营养物污染是全球地表水系统面临的最昂贵和最普遍的环境问题之一。来自农业和城市径流的氮和磷为河流、湖泊和沿海沃茨中藻类的生长提供了燃料。这些藻华耗尽氧气水平，释放出对人类健康有害的毒素。该项目将研究如何实时控制水力基础设施，如水坝，通过模拟流域规模的污水处理厂的过程来处理营养物污染。这种新方法有可能大大减少营养物污染的影响，同时避免昂贵的基础设施扩建项目的需要。这些研究工作将辅之以一项教育计划，旨在通过开发一种针对各级学生的新的计算机模拟策略类游戏，促进积极学习地表水质量概念。研究活动将很好地融入教学和推广计划，通过积极和互动的学习机会进一步传播影响，以扩大对科学，技术，工程和数学（STEM）的参与。该项目将揭示新的基础知识的水力控制对养分动态的影响，使用计算建模和现实世界的实验评估相结合。首先，将开发一个新的计算水质模型，以模拟营养物的命运和运输的河流网络占不稳定的水力学，多物种反应动力学和hyporheic交换。接下来，将研究水质数据同化的新方法，以提供有效控制所需的水质实时估计。第三，最佳水库运行策略的营养物和藻类去除将探索使用鲁棒模型预测控制方法，并在河网规模进行评估。养分动态的建模，估计和控制将使用实验室规模的试验台和系统规模的案例研究，侧重于现实世界的不同大小的流域实测数据进行验证。这项研究将描述和量化水库主动控制的潜力，以减轻流域范围内的营养污染和藻类水华。为了促进广泛的教育推广，这项研究的结果将被整合到一个新的“沙箱”电脑游戏，将模拟营养生态系统在河流网络的相互作用。通过鼓励学生在他们自己的虚拟生态系统中管理水生生物，这个模拟策略类游戏将有机地教授富营养化的机制和预防富营养化所需的干预措施。这个奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持的。