| 项目编号: | 1706343
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| 项目名称: | GOALI: WERF: Towards Energy-saving Wastewater Treatment through High-fidelity Heterogeneity Profiling-based Multiple-zoning Control Methodology |
| 作者: | Baikun Li
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| 承担单位: | University of Connecticut
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| 批准年: | 2017
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| 开始日期: | 2017-08-15
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| 结束日期: | 2020-07-31
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| 资助金额: | 350000
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Engineering - Chemical, Bioengineering, Environmental, and Transport Systems
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| 英文关键词: | wastewater industry
; high-fidelity
; project
; high energy consumption
; energy-positive wastewater treatment
; high-fidelity profiling-based multiple-zone control
; real-time control
; feedback-control methodology
; high school seminar
; high-fidelity profiling
; high-fidelity profile datum
; multiple-zone nonlinear model predictive control
; control methodology
; energy-saving wastewater treatment
; multiple-zone
; high efficiency
; pi
; model predictive multiple-zone control
; wastewater system
; profiling-control technology
; wastewater quality
|
| 英文摘要: | PI Name: Baikun Li
Proposal 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. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/89364
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| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
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| Recommended Citation: | |
Baikun Li. GOALI: WERF: Towards Energy-saving Wastewater Treatment through High-fidelity Heterogeneity Profiling-based Multiple-zoning Control Methodology. 2017-01-01.
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