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，通过复杂的管道网络将饮用水交付给消费者。最近的研究表明，从治疗厂到消费者的水龙头的旅程中，经过处理的饮用水检测质量；这对公共卫生具有重要意义。在预算有限的情况下运行，水公用事业采用了不同程度的水质监测，这些水质监测旨在进行监管合规性，但不适合快速检测和缓解污染事件。此外，公用事业通常缺乏针对性的实时控制策略，并且经常通过发行全能咨询（例如“开水”或“不消耗”咨询服务）来应对污染事件。这些顾问对社会经济的影响有重大影响，通常在提高水质方面速度缓慢。与此相比，该项目通过控制氢化泵，阀门和消毒剂给药站来响应污染事件，从而为城市水网络中的实时水质管理创造了新的方法。为此，这项研究利用了水传感技术的最新进展，同时通过环境科学，优化，网络控制和水生化学研究来研究控制算法。这项研究可以实时监测和控制水的水质和水质，使水公用事业可以采用策略来响应污染事件，并扩大了代表性不足的群体在UT San Antonio，UT Austin和Illinois of Chicago的研究和教育的参与。该项目提出了一个新型的数学框架，该框架与动态水质模型相结合的水解方程，描绘了消毒剂残留物的运输和衰减，这些模型是饮用水分布网络中污染事件检测的代理。最终的框架忠实地描述了在规则条件和污染事件下的网络操作。该框架还可以开发可扩展的优化算法，这些算法适合实时实施，以控制泵，阀门和消毒剂助推器站，从而确保遵守水质标准。该算法旨在处理各种供水系统应用，例如污染物入侵事件的流动调制，响应和恢复，以及可靠的网络范围内的消毒。该理论还对现实的水网络模型进行了评估，除了固定和移动传感器的数据，这些数据来自现实生活中的供水系统收集氢和水质数据。该奖项反映了NSF的法定任务，并通过使用基金会的智力优点和更广泛的影响来评估NSF的法定任务。

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
• 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
• 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.

Revisiting the Water Quality Sensor Placement Problem: Optimizing Network Observability and State Estimation Metrics

• DOI: 10.1061/(asce)wr.1943-5452.0001374
• 发表时间: 2020-12
• 期刊: ArXiv
• 作者: A. Taha;Shen Wang;Yi Guo;T. Summers;Nikolaos Gatsis;M. Giacomoni;Ahmed A. Abokifa

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
• 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.

Model Order Reduction for Water Quality Dynamics

水质动态模型降阶

• 发表时间: 2021
• 期刊: Water resources research
• 影响因子: 5.4
• 作者: Shen Wang, Ahmad F.