CPS: Synergy: Collaborative Research: Cyber-Physical Sensing, Modeling, and Control for Large-Scale Wastewater Reuse and Algal Biomass Production

CPS：协同：协作研究：大规模废水回用和藻类生物质生产的网络物理传感、建模和控制

• 批准号: 1702394
• 负责人: Piya Pal
• 金额: $ 10.58万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1702394&HistoricalAwards=false
• 关键词: CPS; Synergy; Collaborative; Research; Cyber

This project develops advanced cyber-physical sensing, modeling, control, and optimization methods to significantly improve the efficiency of algal biomass production using membrane bioreactor technologies for waste water processing and algal biofuel. Currently, many wastewater treatment plants are discharging treated wastewater containing significant amounts of nutrients, such as nitrogen, ammonium, and phosphate ions, directly into the water system, posing significant threats to the environment. Large-scale algae production represents one of the most promising and attractive solutions for simultaneous wastewater treatment and biofuel production. The critical bottleneck is low algae productivity and high biofuel production cost.The previous work of this research team has successfully developed an algae membrane bioreactor (A-MBR) technology for high-density algae production which doubles the productivity in an indoor bench-scale environment. The goal of this project is to explore advanced cyber-physical sensing, modeling, control, and optimization methods and co-design of the A-MBR system to bring the new algae production technology into the field. The specific goal is to increase the algal biomass productivity in current practice by three times in the field environment while minimizing land, capital, and operating costs. Specifically, the project will (1) adapt the A-MBR design to address unique new challenges for algae cultivation in field environments, (2) develop a multi-modality sensor network for real-time in-situ monitoring of key environmental variables for algae growth, (3) develop data-driven knowledge-based kinetic models for algae growth and automated methods for model calibration and verification using the real-time sensor network data, and (4) deploy the proposed CPS system and technologies in the field for performance evaluations and demonstrate its potentials.This project will demonstrate a new pathway toward green and sustainable algae cultivation and biofuel production using wastewater, addressing two important challenging issues faced by our nation and the world: wastewater treatment and renewable energy. It will provide unique and exciting opportunities for mentoring graduate students with interdisciplinary training opportunities, involving K-12 students, women and minority students. With web-based access and control, this project will convert the bench-scale and pilot scale algae cultivation systems into an exciting interactive online learning platform to educate undergraduate and high-school students about cyber-physical system design, process control, and renewable biofuel production.

该项目开发先进的信息物理传感，建模，控制和优化方法，以显着提高藻类生物质生产的效率，使用膜生物反应器技术进行废水处理和藻类生物燃料。目前，许多废水处理厂将含有大量营养物（如氮、铵和磷酸根离子）的处理后的废水直接排放到水系统中，对环境构成重大威胁。 大规模藻类生产是同时进行废水处理和生物燃料生产的最有前途和最有吸引力的解决方案之一。藻类生产率低和生物燃料生产成本高是其关键瓶颈。本研究团队的前期工作成功开发了藻类膜生物反应器（A-MBR）技术，用于高密度藻类生产，使室内实验室规模环境的生产率提高一倍。该项目的目标是探索先进的信息物理传感，建模，控制和优化方法以及A-MBR系统的协同设计，将新的藻类生产技术带入该领域。具体目标是在田间环境中将当前实践中的藻类生物质生产力提高三倍，同时最大限度地减少土地，资本和运营成本。具体而言，该项目将（1）调整A-MBR设计，以应对田间环境中藻类培养的独特新挑战，（2）开发多模态传感器网络，用于实时原位监测藻类生长的关键环境变量，（3）开发数据驱动的基于知识的藻类生长动力学模型，以及使用实时传感器网络数据进行模型校准和验证的自动化方法，以及（4）将建议的CPS系统和技术部署在现场进行性能评估并展示其潜力。该项目将展示一条通往绿色和可持续藻类养殖以及利用废水生产生物燃料的新途径，解决我国和世界面临的两个重要挑战性问题：废水处理和可再生能源。它将提供独特和令人兴奋的机会，指导研究生与跨学科的培训机会，涉及K-12学生，妇女和少数民族学生。通过基于网络的访问和控制，该项目将把实验室规模和中试规模的藻类培养系统转换成一个令人兴奋的交互式在线学习平台，以教育本科生和高中生关于网络物理系统设计，过程控制和可再生生物燃料生产。