Collaborative Research: Joint Control of Hydraulics and Water Quality Dynamics in Drinking Water Networks

合作研究：饮用水管网水力学和水质动态的联合控制

• 批准号: 2015671
• 负责人: Ahmad Taha
• 金额: $ 25.09万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015671&HistoricalAwards=false
• 关键词: Collaborative; Research; Joint; Control; Hydraulics

After leaving water treatment facilities, drinking water is delivered to consumers through an intricate network of pipes. Recent studies have demonstrated that the quality of treated drinking water deteriorates during the journey from treatment plants to consumers’ taps; this has significant implications for public health. Operating under limited budgets, water utilities adopt different degrees of water quality monitoring that are geared towards regulatory compliance but are unbefitting for rapid detection and mitigation of contamination events. Furthermore, utilities typically lack targeted, real-time control strategies and often respond to contamination events by issuing utility-wide advisories (e.g., “boil water” or “do not consume” advisories). Such advisories have significant socio-economic impacts and are typically slow in improving water quality. In contrast to this, this project creates new methods for real-time water quality management in urban water networks by controlling hydraulic pumps, valves, and disinfectant dosing stations in response to contamination events. To this end, this research harnesses recent advances in water sensing technologies while investigating control algorithms through interdisciplinary research from environmental science, optimization, network control, and aquatic chemistry. This research enables real-time monitoring and control of hydraulics and water quality, allows water utilities to adopt strategies in response to contamination events, and broadens participation of underrepresented groups in research and education at UT San Antonio, UT Austin and the University of Illinois at Chicago. This project puts forth a novel mathematical framework that couples the hydraulic equations governing water flow and pressure with dynamic water quality models depicting the transport and decay of disinfectant residuals, which act as a proxy for contamination event detection in drinking water distribution networks. The resulting framework faithfully describes the network operation under regular conditions and contamination events. This framework also enables the development of scalable optimization algorithms that are amenable to real-time implementation for control of pumps, valves, and disinfectant booster stations, thereby ensuring compliance with water quality standards. The algorithms are designed to deal with a variety of water system applications such as flow modulation, response and recovery from contaminant intrusion events, and reliable network-wide disinfection. The theory is evaluated on realistic water network models in addition to data from fixed and mobile sensors that collect hydraulic and water quality data from a real-life water system.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.

离开水处理设施后，饮用水通过复杂的管道网络输送给消费者，最近的研究表明，经过处理的饮用水在从处理厂到消费者水龙头的过程中质量会恶化，这对公众健康产生重大影响。在预算有限的情况下，自来水公司采用了不同程度的水质监测，这些监测旨在满足法规要求，但不适合快速检测和减轻污染事件。此外，公用事业公司通常缺乏有针对性的实时控制策略，并且经常对污染事件做出反应。通过在公用事业范围内发布建议（例如，“煮水”或“不要饮用”建议）具有重大的社会经济影响，并且通常在改善水质方面进展缓慢，与此相反，该项目创建了实时水质的新方法。为此，本研究利用水传感技术的最新进展，同时通过环境科学、优化、网络控制等跨学科研究来研究控制算法。 ， 和这项研究能够实时监测和控制水力学和水质，使自来水公司能够采取应对污染事件的策略，并扩大代表性不足的群体对德克萨斯大学圣安东尼奥分校、德克萨斯大学奥斯汀分校和大学研究和教育的参与。该项目提出了一种新颖的数学框架，将控制水流和压力的水力方程与描述消毒剂残留物的传输和衰减的动态水质模型结合起来，作为饮用水分配中污染事件检测的代理。由此产生的框架忠实地描述了常规条件和污染事件下的网络运行情况，该框架还能够开发可扩展的优化算法，这些算法适合实时实现泵、阀门和消毒剂增压站的控制，从而确保合规性。这些算法旨在处理各种水系统应用，例如流量调节、污染物入侵事件的响应和恢复以及可靠的网络范围内的消毒。来自固定和移动传感器的数据，这些传感器从现实生活中的水系统收集水力和水质数据。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Comprehensive Framework for Controlling Nonlinear Multi-species Water Quality Dynamics

控制非线性多物种水质动态的综合框架

• 发表时间: 2023-07
• 期刊: Journal of water resources management
• 作者: Elsherif, Salma;Taha, Ahmad;Abokifa, Ahmed;Sela, Lina
• 通讯作者: Sela, Lina

Comprehensive Framework for Controlling Nonlinear Multi-species Water Quality Dynamics

控制非线性多物种水质动态的综合框架

• 发表时间: 2023-07
• 期刊: Journal of water resources planning and management
• 影响因子: 3.1
• 作者: Elsherif, Salma;Taha, Ahmad;Abokifa, Ahmed;Sela, Lina
• 通讯作者: Sela, Lina

How Effective is Model Predictive Control in Real‐Time Water Quality Regulation? State‐Space Modeling and Scalable Control

模型预测控制在实时水质调节中的效果如何？

• DOI: 10.1029/2020wr027771
• 发表时间: 2021-05
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: Wang, Shen;Taha, Ahmad F.;Abokifa, Ahmed A.
• 通讯作者: Abokifa, Ahmed A.

Model Order Reduction for Water Quality Dynamics

水质动态模型降阶

• 发表时间: 2021-08
• 期刊: Water resources research
• 影响因子: 5.4
• 作者: Shen Wang; Ahmad F.
• 通讯作者: Ahmad F.

Bayesian Optimization of Booster Disinfection Scheduling in Water Distribution Networks

配水管网增压消毒调度的贝叶斯优化

• DOI: 10.1016/j.watres.2023.120117
• 发表时间: 2023-08
• 期刊: Water Research
• 影响因子: 12.8
• 作者: Moeini, Mohammadreza;Sela, Lina;Taha, Ahmad F.;Abokifa, Ahmed A.
• 通讯作者: Abokifa, Ahmed A.