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.

这项教师早期职业发展（职业）奖支持研究，该研究将探讨实时控制水解基础设施的潜力，以减少河网络中的营养污染。养分污染是全球地表水系统面临的最昂贵，最广泛的环境问题之一。农业和城市径流中的氮和磷为河流，湖泊和沿海水域中藻类的生长增长。这些藻类血液耗尽氧气水平并释放对人类健康有害的毒素。该项目将研究如何通过在流域量表上模仿废水处理厂的过程来处理氢化基础设施（如大坝）的实时控制能够治疗营养污染。这种新方法有可能大大减少营养污染的影响，同时消除对昂贵的基础设施扩展项目的需求。这些研究工作将通过一项教育计划来完成，旨在通过开发针对各个级别学生的新计算机模拟游戏来促进地表水质概念的积极学习。研究活动将很好地整合到教学和外展计划中，以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。该项目将通过计算建模和现实世界实验评估的组合揭示有关氢化控制对养分动力学影响的新基本知识。首先，将开发出一种新的计算水质模型，以模拟河流网络中的营养命运和运输，以构成不稳定的水力学，多种物种反应动力学和低潮交换。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的实时估计。第三，将使用强大的模型预测控制方法探索营养和藻类去除的最佳储层操作策略，并在河网量表上进行评估。使用实验室规模的测试和系统规模的案例研究，将对养分动力学的建模，估计和控制进行验证。这项研究将表征和量化积极控制储层以减轻营养污染和藻类血液的潜力。为了促进广泛的教育外展，这项研究的结果将集成到新的“沙盒”计算机游戏中，该游戏将模拟河网络中的营养生态系统相互作用。通过鼓励学生在自己的虚拟生态系统中管家水生生活，该模拟游戏将有机地教授富营养化的机制和预防它所需的干预措施。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响审查审查标准来通过评估来诚实地支持我们的支持。