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

许多小社区拥有和运营小型分散的废水处理设施，其中许多设施陈旧，不够灵活，无法适应不同水质的处理。这些社区中的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都是全自动化的，包括小厂，但它们对故障的敏感性很高，快速恢复和恢复运行的能力很低。在这个项目中，研究团队将开发一种创新的智能监测和控制系统，以早期发现小型设施中的废水处理系统故障，并为小型分散的废水处理系统提供低成本的远程监测和控制系统。水的回收和再利用并不是什么新鲜事，但关于水再利用新模式的讨论正在全国范围内加速，例如直接饮用水再利用。因此，当水源明显受损，注定会成为饮用水，甚至是其他有益用途的水时，水质监测、处理系统故障的早期预警、应急操作和知情的公众都是确保未来水资源和保护公众和环境的关键。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、连续地运行，而不会对人和环境造成负面影响。工厂运营商和公众比以往任何时候都更了解情况，必须有更好的工具来了解水质和家庭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中心的系统将提供这些工具，并刺激能效系统行为。将使用独特的试验台进行这项研究。它由一个先进的序批式膜生物反应器(SB-MBR)复合系统组成，每天处理7000 Gal/d的实际生活污水。研究小组将利用这一平台整合现有和新的无线传感器网络，以监测水质和进行过程监测和控制，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。智能服务系统将实现污水处理厂故障的早期预警，从而防止长期恢复和对社区服务的负面影响。试验台有五个独特的组成部分：一个演示规模的先进水回收系统，一个包含尖端分析探头和仪器的新型传感器网络，一个新颖的数据处理和自学习控制系统，能源管理优化模块，以及一个公共互动中心。它还将使水处理到不同的最终质量，以生产不同的再利用应用的水(即，量身定制的水再利用)。这种新一代智能定制水回用系统将拥有灵活和适应性强的控制系统，这些控制系统利用新的智能传感器技术，相互作用，从过去的性能中学习，可以预测未来的性能，并使系统实现预设目标。在对新的监测和控制系统进行示范规模测试后，该团队将与他们的工业合作伙伴合作，在现有的分散处理厂部署、整合和测试新系统。该项目由科罗拉多矿业学院(土木工程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)领导。水中有氧系统(AAS)公司(Rockford IL；Small Business)和Kennedy/Jenks Consulting(加利福尼亚州旧金山；Small Business)是主要的工业合作伙伴。其他更广泛的背景合作伙伴包括GE Power&amp；Water(博尔德，CO)、Ramey Environmental(Firestone，CO)和南内华达水务局(拉斯维加斯，内华达州)。

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).