PFI:BIC: Self-Correcting Energy-Efficient Water Reclamation Systems for Tailored Water Reuse at Decentralized Facilities

PFI:BIC：自校正节能水回收系统，用于分散设施的定制水回用

基本信息

批准号：
1632227
负责人：
Tzahi Cath
金额：
$ 95.99万
依托单位：
Colorado School of Mines
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1632227&HistoricalAwards=false
关键词：
PFI BIC Self Correcting Energy

项目摘要

Many small communities own and operate small, decentralized wastewater treatment facilities, many of which are old and not flexible enough to adjust for treatment of variable water quality. Many of these communities do not have the resources to improve the treatment system or comply with new discharge regulations. While most wastewater treatment plants are fully automated, including small plants, their susceptibility to failure are high and their ability to quickly recover and resume operation are low. In this project the research team will be developing an innovative smart monitoring and control system to provide early detection of wastewater treatment system failure at small facilities and low-cost, remote monitoring and control systems for small, decentralized wastewater treatment systems.Water reclamation and reuse is not new, but discussions about new paradigms in water reuse, such as direct potable reuse, are accelerating across the country. Thus, when the source of water is explicitly impaired and it is destined to become drinking water, or even water for other beneficial applications, monitoring of water quality, early warning of treatment system failure, responsive operation, and an informed public are all critical to securing future water resources and protecting the public and the environment. A smart sensor network supported by smart data acquisition/processing and system-learning programs will ensure that next generation wastewater treatment systems can operate sustainably and continuously without negative impact on people and the environment. More than ever, plant operators and the public are highly informed and must have better tools to understand water quality and economics of domestic water reuse, and the negative impacts of water contamination. The human-centered system that will be developed through this project will provide these tools and stimulate energy efficiency system behaviors.A unique testbed will be used to conduct this research. It consists of an advance sequencing batch membrane bioreactor (SB-MBR) hybrid system treating 7,000 gal/day of real domestic wastewater. The research team will use this platform to integrate existing and new wireless sensor networks to monitor water quality and for process monitoring and control, to facilitate and test the development of a smart data acquisition/processing and self-learning control system. The smart service system will enable early warning of wastewater treatment plant failure, thus preventing long-term recovery and negative impact on community services. The testbed has five distinctive components: a demo-scale, advanced water reclamation system, a novel sensor network incorporating cutting edge analytical probes and instruments, a novel data processing and self-learning control system, energy management optimization module, and a public interaction center. It will also enable treatment of water to different end quality to produce water for different reuse applications (i.e., tailored water reuse). This new generation, smart system for tailored water reuse will have flexible and adaptable control systems that utilize new, smart sensor technologies, which interact with each other, learn from past performance, and can predict future performance and adapt the system to achieve preset objectives. After testing the new monitoring and control system at a demonstration scale, the team will work with their industrial partners to deploy, incorporate, and test the novel system at existing, decentralized treatment plants. This project is led by the Colorado School of Mines (Department of Civil & Environmental Engineering and Department of Electrical Engineering & Computer Science) and Baylor University (Department of Applied Mathematic and Statistics). Aqua-Aerobic Systems (AAS), Inc. (Rockford IL; small business) and Kennedy/Jenks Consulting (San Francisco, CA; small business) are the primary industrial partners. Additional broader context partners include GE Power & Water (Boulder, CO), Ramey Environmental (Firestone, CO), and Southern Nevada Water Authority (Las Vegas, NV).

许多小社区拥有并经营小型、分散的废水处理设施，其中许多设施陈旧且不够灵活，无法适应不同水质的处理。其中许多社区没有资源来改善处理系统或遵守新的排放法规。虽然大多数废水处理厂（包括小型工厂）都是完全自动化的，但它们发生故障的可能性很高，并且快速恢复和恢复运行的能力较低。在该项目中，研究团队将开发一种创新的智能监测和控制系统，以早期检测小型设施的废水处理系统故障，并为小型、分散式废水处理系统提供低成本、远程监测和控制系统。水回收和再利用这并不新鲜，但关于水再利用新范式（例如直接饮用水再利用）的讨论正在全国范围内加速。因此，当水源明显受损，并且注定要成为饮用水，甚至是其他有益应用的水时，水质监测、处理系统故障预警、响应性操作和公众知情都至关重要。确保未来的水资源并保护公众和环境。由智能数据采集/处理和系统学习程序支持的智能传感器网络将确保下一代废水处理系统能够可持续、持续运行，而不会对人类和环境产生负面影响。工厂经营者和公众比以往任何时候都更加了解情况，必须拥有更好的工具来了解水质和生活用水再利用的经济性以及水污染的负面影响。通过该项目开发的以人为本的系统将提供这些工具并刺激能源效率系统行为。将使用一个独特的测试平台来进行这项研究。它由先进的序批式膜生物反应器 (SB-MBR) 混合系统组成，每天可处理 7,000 加仑的实际生活废水。研究团队将利用该平台集成现有和新的无线传感器网络来监测水质和过程监测和控制，以促进和测试智能数据采集/处理和自学习控制系统的开发。智能服务系统将能够对污水处理厂故障进行早期预警，从而防止长期恢复和对社区服务的负面影响。该试验台有五个独特的组成部分：演示规模的先进水回收系统、包含尖端分析探针和仪器的新型传感器网络、新型数据处理和自学习控制系统、能源管理优化模块以及公共交互中心。它还可以将水处理成不同的最终质量，以生产用于不同再利用应用的水（即定制水再利用）。这种用于定制水回用的新一代智能系统将具有灵活且适应性强的控制系统，该系统利用新的智能传感器技术，这些技术彼此交互，从过去的性能中学习，并可以预测未来的性能并调整系统以实现预设目标。在示范规模测试新的监测和控制系统后，该团队将与工业合作伙伴合作，在现有的分散处理厂部署、整合和测试该新型系统。该项目由科罗拉多矿业学院（土木与环境工程系和电气工程与计算机科学系）和贝勒大学（应用数学与统计系）牵头。 Aqua-Aerobic Systems (AAS), Inc.（伊利诺伊州罗克福德；小型企业）和 Kennedy/Jenks Consulting（加利福尼亚州旧金山；小型企业）是主要的工业合作伙伴。其他更广泛的合作伙伴包括 GE Power & Water（科罗拉多州博尔德）、Ramey Environmental（科罗拉多州费尔斯通）和南内华达水务局（内华达州拉斯维加斯）。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Hybrid statistical-machine learning ammonia forecasting in continuous activated sludge treatment for improved process control

连续活性污泥处理中的混合统计机器学习氨预测，以改进过程控制

DOI：
10.1016/j.jwpe.2020.101389
发表时间：
2020-10
期刊：
Journal of Water Process Engineering
影响因子：
7
作者：
Newhart, Kathryn B.;Marks, Christopher A.;Rauch;Cath, Tzahi Y.;Hering, Amanda S.
通讯作者：
Hering, Amanda S.

Multistate multivariate statistical process control

多状态多元统计过程控制

DOI：
10.1002/asmb.2333
发表时间：
2018-11-01
期刊：
Applied Stochastic Models in Business and Industry
影响因子：
1.4
作者：
Gabriel J. Odom;Kathryn B. Newhart;T. Cath;A. Hering
通讯作者：
A. Hering

Case studies in real-time fault isolation in a decentralized wastewater treatment facility

分散式废水处理设施中实时故障隔离的案例研究

DOI：
10.1016/j.jwpe.2020.101556
发表时间：
2020-12-01
期刊：
Journal of water process engineering
影响因子：
0
作者：
M. Kl;erman;erman;Kathryn B. Newhart;T. Cath;A. Hering
通讯作者：
A. Hering

Prediction of Peracetic Acid Disinfection Performance for Secondary Municipal Wastewater Treatment Using Artificial Neural Networks

利用人工神经网络预测城市污水二级处理过氧乙酸消毒性能

DOI：
10.1021/acsestwater.0c00095
发表时间：
2021-02
期刊：
ACS ES&T Water
影响因子：
0
作者：
Newhart, Kathryn B.;Goldman;Freedman, Daniel E.;Wisdom, K. Blair;Hering, Amanda S.;Cath, Tzahi Y.
通讯作者：
Cath, Tzahi Y.

Fault Isolation for A Complex Decentralized Waste Water Treatment Facility

复杂的分散式废水处理设施的故障隔离