GOALI: Developing Sensor-Mediated Control Strategies that Allow Innovative Treatment of Nitrogen in Wastewater

1438560 LoveGOALI：开发传感器介导的控制策略，使创新的处理氮在污水这一建议汇集了汉普顿道路卫生区和密歇根大学开发传感器介导的控制策略，实现有效的氮去除下一代主流污水系统，并证明该战略通过试点规模的系统。正在考虑的脱氮技术是新颖的，尚未开发的主流实施，但通过显着减少能源，碳和土地足迹的处理过程，满足严格的营养法规，为美国的很大一部分具有很大的希望废水流量。为了充分了解传感器介导的控制策略如何赋予包括这些处理系统的微生物群落持续的性能和弹性，需要强大的大学合作伙伴关系。在正在评估的生物反应器中，密歇根大学将负责进行分子微生物学分析，以评估传感器介导的控制策略对微生物聚生体的组成和代谢功能的影响。实验室规模的系统将在密歇根大学运行，而现有的试点规模的系统将在密歇根大学和汉普顿道路卫生区运行。所有的实验系统将完全自动化与传感器介导的控制策略。基于性能的计算模型将在第一年开发，并在随后的几年中进一步完善，以开发一个有用的设计工具，供行业使用，一旦这些技术得到实施。一个或多个研究生将资助这个项目，并将在校园内和汉普顿道路卫生区在他们的博士课程工作。学位课程工业co-PI将担任研究生的共同顾问。该项目将展示一个公用事业和大学的研究伙伴关系，将作为一个国家的模式。有一个快速发展的侧流处理含氨废水，使用亚硝酸盐抑制，以防止完全氨氧化，和厌氧氨氧化（anammox），以消除氮。然而，主流处理的氨浓度低一个数量级以上，流量大得多的类似的发展还没有发生。有独特的挑战，实施传感器介导的部分氨氧化，亚硝酸盐抑制（NOB淘汰）和厌氧氨氧化在主流，将通过这个项目来解决。PI还将解决厌氧甲烷氧化，一种新的微生物代谢，以及厌氧硫化物氧化。该团队将评估传感器介导的控制策略对组成这些系统的微生物群落的结构和功能的影响。反过来，这将促进我们更广泛的理解传感器介导的控制策略，废水处理的机械水平。由于在下一代治疗系统中推进传感器介导控制策略的使用方面的合作努力，研究团队更有可能从传统上对先进自动化持谨慎态度的行业中获得“支持”。

1438560LoveGOALI: Developing Sensor-Mediated Control Strategies that Allow Innovative Treatment of Nitrogen in WastewaterThis proposal brings together Hampton Roads Sanitation District and the University of Michigan to develop a sensor-mediated control strategy for achieving efficient nitrogen removal from next generation mainstream wastewater systems, and to demonstrate that strategy through pilot-scale systems. The nitrogen removal technologies being considered are novel and undeveloped for mainstream implementation, but hold great promise by significantly reducing the energy, carbon and land footprint of treatment processes that meet stringent nutrient regulations for a significant percentage of the U.S. wastewater flow. To fully understand how sensor-mediated control strategies confer sustained performance and resiliency to microbiological communities that comprise these treatment systems, a strong university partnership is needed. In the bioreactors being evaluated, University of Michigan will be responsible for performing molecular microbiological analyses to evaluate the impact of sensor-mediated control strategies on the composition and metabolic function of the microbial consortia. Experimental lab-scale systems will be operated at University of Michigan while existing pilot-scale systems will be operated at both University of Michigan and Hampton Roads Sanitation District. All experimental systems will be fully automated with sensor-mediated control strategies. Performance-based computational models will be developed during the first year and further refined in subsequent years to develop a useful design tool for the industry to use once these technologies are implemented. One or more graduate students will be funded on this project and will work both on-campus and at Hampton Roads Sanitation District over the course of their Ph.D. degree program. The industrial co-PI will serve as co-advisor to the graduate student(s). This project will demonstrate a utility-university research partnership that will serve as a national model.There has been rapid development of sidestream treatment of ammonia-ladened wastewaters that use nitrite repression to prevent complete ammonia oxidation, and anaerobic ammonia oxidation (anammox) to remove N. However, comparable development for mainstream treatment where the ammonia concentrations are more than an order of magnitude lower and flows are vastly larger has not occurred. There are unique challenges to implementing sensor-mediated partial ammonia oxidation, nitrite repression (NOB out-selection) and anammox at the mainstream that will be addressed through this project. The PIs will also address denitrifying anaerobic methane oxidation, a new microbial metabolism, as well as anaerobic sulfide oxidation. The team will evaluate the impact that sensor-mediated control strategies have on the structure and function of microbial communities that comprise these systems. This, in turn, will advance our broader understanding of sensor-mediated control strategies for wastewater treatment at a mechanistic level. Due to the collaborative effort in advancing use of sensor-mediated control strategies with next-generation treatment systems, the research team is more likely to get "buy-in" from an industry that has traditionally been wary of advanced automation.