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

PI名称：白坤液建议编号：1706343高能耗是困扰污水处理行业的一个长期问题。使用单点探头监测的生物养分去除(BNR)系统提供了运行状态和废水水质的不完整图景。PI试图通过结合现场分析、建模和反馈控制方法来解决这一问题。目标是将BNR从一个高耗能、低效率和不稳定的系统转变为一个精确控制、节能、动态、健壮的系统。行业实习计划将有助于培养一支特别重视创新和创业的劳动力队伍。包括培训研讨会和高中研讨会在内的多项推广活动将向学生，特别是来自代表人数不足的群体的学生介绍环境友好技术。该项目的目标是通过三种创新解决方案实现节能废水处理：使用微电极阵列(MEA)对不同过程进行高保真分析、数据驱动建模和高级模型预测性多区域控制。通过将硝化作用作为试验台，PIS将执行四个交互任务：1)在实验室规模的硝化系统中进行7个关键参数(溶解氧、氧化还原电位、pH、温度、电导率、氨氮和硝酸盐)的MEA剖面分析，以获得高保真剖面数据；2)基于分布的MEA剖面图，开发数据驱动模型，描述系统中的物理化学过程，预测不同条件下的能耗和硝化效率；3)将开发多区域非线性模型预测控制方法，以实现关键运行参数的精确调整，并在每个区域执行实时控制，以保持高效和稳定；以及4)将在工业伙伴S现场的中试硝化系统中演示基于高保真轮廓的多区域控制，以检验其在真实场景中的准确性。这一创新的概况分析和控制方法可能会改变废水系统的设计、工程和管理，并有可能实现节能的废水处理。总体而言，该项目针对废水行业能耗高的问题，并将为确保在一个易于部署的平台上进行节能设计做出重大贡献。行业合作伙伴将通过评估拟议的剖面控制技术的可扩展性并加快将学术发现转化为废水行业来为项目增值。该项目的成果将适用于广泛的最终用途，从而使废水处理行业普遍受益。

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.