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

该项目开发先进的网络物理传感、建模、控制和优化方法，以显著提高利用膜生物反应器技术生产藻类生物质的效率，用于废水处理和藻类生物燃料。目前，许多污水处理厂将含有大量氮、铵、磷等营养物质的处理废水直接排放到水系统中，对环境构成严重威胁。大规模藻类生产是废水处理和生物燃料生产同时进行的最有前景和最有吸引力的解决方案之一。藻类生产的关键瓶颈是藻类生产率低和生物燃料生产成本高。该研究小组之前的工作成功地开发了藻类膜生物反应器(A-MBR)技术，用于高密度藻类生产，使室内实验室规模的藻类生产效率翻了一番。本项目的目标是探索先进的数字物理传感、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引入该领域。具体目标是将当前实践中的藻类生物量生产力提高三倍，同时将土地、资本和运营成本降至最低。具体地说，该项目将(1)采用A-MBR设计以应对野外环境中藻类培养的独特新挑战，(2)开发用于实时监测藻类生长的关键环境变量的多模式传感器网络，(3)开发基于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)在现场部署拟议的CPS系统和技术以进行性能评估并展示其潜力。该项目将展示一条绿色和可持续的藻类培养和利用废水生产生物燃料的新途径，解决我们国家和世界面临的两个重要挑战问题：废水处理和可再生能源。它将为指导研究生提供独特和令人兴奋的机会，提供跨学科培训机会，涉及K-12学生、女性和少数族裔学生。通过基于网络的访问和控制，该项目将把实验室规模和试点规模的藻类培养系统转变为一个令人兴奋的互动在线学习平台，教育本科生和高中生有关网络物理系统设计、过程控制和可再生生物燃料生产的知识。

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.