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

项目名称：李柏坤提案编号：1706343高能耗是污水处理行业长期存在的问题。使用单点探针监测的生物营养物去除（BNR）系统给出了运行状态和废水质量的不完整图像。pi试图通过结合原位分析、建模和反馈控制方法来解决这个问题。目标是将BNR从一个能源密集型、低效和不稳定的系统转变为一个精确控制、节能、动态、鲁棒的系统。该行业实习项目将帮助培养一支特别注重创新和创业精神的劳动力队伍。包括培训讲习班和高中研讨会在内的多项外联倡议将向学生，特别是来自代表性不足群体的学生介绍无害环境的技术。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：使用毫微米电极阵列（MEA）对异构过程进行高保真分析，数据驱动建模和先进的模型预测多区域控制。通过将硝化作为实验平台，pi将进行四项交互任务：1)在实验室规模的硝化系统中对七个关键参数（溶解氧、氧化还原电位、pH、温度、电导率、铵态氮和硝酸盐）进行MEA分析，以获得高保真的剖面数据；2)基于分布式MEA剖面建立数据驱动模型，描述系统中的物理和化学过程，预测不同条件下的能耗和硝化效率；3)开发多区域非线性模型预测控制（NMPC）方法，实现关键运行参数的精确调整，并对每个区域进行实时控制，以保持高效率和稳定性；4)基于高保真剖面的多区域控制将在工业合作伙伴的中试硝化系统中进行演示。S站点以检查其在真实场景中的准确性。这种创新的分析和控制方法将潜在地改变废水系统的设计、工程和管理，实现节能废水处理的可能性。总体而言，该项目针对废水行业的高能耗问题，并将在一个易于部署的平台上为确保节能设计做出重大贡献。行业合作伙伴将通过评估所提议的分析控制技术的可扩展性，并加速将学术发现转化为废水行业，从而为项目增加价值。该项目的成果将适用于广泛的最终用途应用，从而为污水处理行业带来普遍利益。

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.