GOALI: WERF: Towards Energy-saving Wastewater Treatment through High-fidelity Heterogeneity Profiling-based Multiple-zoning Control Methodology

目标：WERF：通过基于高保真异质性分析的多分区控制方法实现节能废水处理

• 批准号: 1706343
• 负责人: Baikun Li
• 金额: $ 35万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1706343&HistoricalAwards=false
• 关键词: GOALI; WERF; Towards; Energy; saving

PI Name: Baikun LiProposal Number: 1706343 High energy consumption is a long-standing problem for the wastewater industry. Biological nutrient removal (BNR) systems monitored using single-point probes give an incomplete picture of the operational status and wastewater quality. The PIs seek to address this problem through a combination of in situ profiling, modeling, and feedback-control methodology. An objective is to transform BNR from an energy-intensive, inefficient, and unstable system to a precisely controlled, energy-saving, dynamic, robust system. The industry internship program will help create a workforce with special emphasis on innovation and entrepreneurship. Multiple outreach initiatives including training workshops and high school seminars will introduce students, especially those from underrepresented groups, to environmentally friendly technologies.The goal of this project is to achieve energy-saving wastewater treatment through three innovative solutions: high-fidelity profiling of heterogeneous processes using milli-electrode array (MEA), data-driven modeling, and advanced model predictive multiple-zone control. By using nitrification as the testbed, the PIs will conduct four interactive tasks: 1) MEA profiling of seven critical parameters (dissolved oxygen, redox potential, pH, temperature, conductivity, ammonium and nitrate) will be conducted in a lab-scale nitrification system to obtain high-fidelity profile data; 2) Data-driven models will be developed based on the distributed MEA profiles to describe the physical and chemical process in the system and predict energy consumption and nitrification efficiency under varying conditions; 3) Multiple-zone nonlinear model predictive control (NMPC) methodology will be developed to enable precise adjustment of critical operational parameters and execute real-time control in each zone to maintain a high efficiency and stability; and 4) High-fidelity profiling-based multiple-zone control will be demonstrated in a pilot-scale nitrification system at the industrial partner?s site to examine its accuracy in a real-world scenario. This innovative profiling and control methodology will potentially transform the design, engineering, and management of wastewater systems with the possibility of achieving energy-positive wastewater treatment. Overall, the project targets the problem of high energy consumption in the wastewater industry and will make a significant contribution to ensure energy-saving design in an easily deployable platform. The industry partner will add value to the project by evaluating the scalability of the proposed profiling-control technology and accelerating the translation of academic discoveries to the wastewater industry. The outcomes of the project will be appropriate for a broad spectrum of end-use applications and thus provide general benefit the wastewater industry.

PI名称：Baikun脂质面包数：1706343高能量消耗是废水行业的长期问题。使用单点探针监测的生物营养清除（BNR）系统，使操作状态和废水质量的情况不完整。 PI寻求通过原位分析，建模和反馈控制方法的结合来解决这个问题。一个目标是将BNR从能源密集型，低效且不稳定的系统转变为精确控制的，节能，动态，健壮的系统。该行业实习计划将有助于创建一名劳动力，并特别强调创新和企业家精神。包括培训研讨会和高中研讨会在内的多项宣传计划将介绍学生，特别是代表性不足的群体的学生，向环保技术介绍。该项目的目的是通过三种创新的解决方案来实现节能废水处理：使用Milli-Electrode Arroy（Mea）的多种元素过程（MILI-ElectRode Aryane（Mea）多重模型，并模型模型。通过使用硝化作用作为测试床，PI将执行四个交互任务：1）将在实验室尺寸的硝化系统中进行七个关键参数（溶解氧，氧化还原电位，pH，温度，电导率，铵和硝酸盐）的MEA分析（溶解氧，pH，温度，电导率，铵和硝酸盐），以获得高尺寸的概况数据； 2）数据驱动的模型将根据分布式MEA剖面开发，以描述系统中的物理和化学过程，并预测各种条件下的能源消耗和硝化效率； 3）将开发多区域非线性模型预测控制（NMPC）方法，以精确调整关键操作参数并在每个区域中执行实时控制，以保持高效率和稳定性； 4）将在工业合作伙伴站点的试验尺度硝化系统中证明基于高保真分析的多区域控制，以在现实世界中检查其准确性。这种创新的分析和控制方法将有可能改变废水系统的设计，工程和管理，并有可能实现能量阳性废水处理。总体而言，该项目针对废水行业的高能消耗问题，并将做出重大贡献，以确保在易于部署的平台中省力设计。该行业合作伙伴将通过评估拟议的分析控制技术的可扩展性，并加速将学术发现转换为废水行业，从而为项目增加价值。 该项目的结果适用于广泛的最终用途应用程序，从而为废水行业提供一般利益。

项目成果

Towards high resolution monitoring of water flow velocity using flat flexible thin mm-sized resistance-typed sensor film (MRSF)

• DOI: 10.1016/j.wroa.2019.100028
• 发表时间: 2019-04
• 期刊: Water Research X
• 影响因子: 7.5
• 作者: Zhiheng Xu;Yingzheng Fan;Tianbao Wang;Yuankai Huang;Farzaneh MahmoodPoor Dehkordy;Zheqin Dai;Lingling Xia;Qiuchen Dong;A. Bagtzoglou;J. McCutcheon;Yu Lei;Baikun Li

Ion selective nano-mesh electrode for long-term continuous monitoring of wastewater quality fabricated using template-guided membrane immobilization

• DOI: 10.1039/d1en00966d
• 发表时间: 2022
• 期刊: Environmental Science: Nano
• 作者: Tianbao Wang;Can Cui;Yuankai Huang;Yingzheng Fan;Zhiheng Xu;Logan Sarge;C. Bagtzoglou;C. Brückner;Puxian Gao;Baikun Li

High-fidelity profiling and modeling of heterogeneity in wastewater systems using milli-electrode array (MEA): Toward high-efficiency and energy-saving operation

使用毫电极阵列 (MEA) 对废水系统中的异质性进行高保真分析和建模：实现高效节能运行

• DOI: 10.1016/j.watres.2019.114971
• 发表时间: 2019
• 期刊: Water Research
• 影响因子: 12.8
• 作者: Xu, Zhiheng;MahmoodPoor Dehkordy, Farzaneh;Li, Yan;Fan, Yingzheng;Wang, Tianbao;Huang, Yuankai;Zhou, Wangchi;Dong, Qiuchen;Lei, Yu;Stuber, Matthew D.
• 通讯作者: Stuber, Matthew D.

Solving Sensor Reading Drifting Using Denoising Data Processing Algorithm (DDPA) for Long-Term Continuous and Accurate Monitoring of Ammonium in Wastewater

• DOI: 10.1021/acsestwater.0c00077
• 发表时间: 2020-12
• 期刊: ACS ES&T Water
• 作者: Xingyu Wang;Yingzheng Fan;Yuankai Huang;Jing Ling;A. Klimowicz;Grace Pagano;Baikun Li

Electrospraying Zwitterionic Copolymers as an Effective Biofouling Control for Accurate and Continuous Monitoring of Wastewater Dynamics in a Real-Time and Long-Term Manner

• DOI: 10.1021/acs.est.2c01501
• 发表时间: 2022-06-21
• 期刊: ENVIRONMENTAL SCIENCE & TECHNOLOGY
• 影响因子: 11.4
• 作者: Huang, Yuankai;Qian, Xin;Li, Baikun
• 通讯作者: Li, Baikun