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Mueller to Work on $2.6M DARPA Collaboration Grant
CEE/COS Assistant Professor Amy Mueller, in collaboration with University of Washington and CalTech, will work on a $2.6M DARPA grant for the "Implementation of a synthetic Anammox bio-granular technology in the main wastewater treatment line." With collaborators Mari Winkler (UW - PI), David Stahl (UW), and Rustem F. Ismagilov (CalTech), the grant is funded by DARPA's Biological Robustness in Complex Settings (BRICS).
Motivation: Water reclamation facilities consume approximately 2-4 percent of the annual US energy budget, more than half of which is due to aeration energy requirements. Anammox granular sludge technologies for nitrogen removal offer a 50 percent reduction in aeration demand and 75 percent reduction in space requirements, resulting in 30 percent lower operating costs compared to conventional treatment; however, current Anammox technologies can only be applied to warm ammonium-rich side-streams (ca. 1 percent of total volumetric flow), limiting its practical impact. The focus of this work is, therefore, to enable Anammox in the cold main-treatment stream (100 percent of flow). In side-stream systems ammonium oxidizing bacteria (AOB) supply nitrite for Anammox while the competing activity of nitrite oxidizing bacteria (NOB) is suppressed. Our team will develop a more robust configuration, which can be implemented directly at low ammonia concentrations and cold temperatures, by combining Anammox with an organism that can supply nitrite without competing with NOB for substrate. Ammonium-oxidizing archaea (AOA) have much higher affinities for dissolved oxygen and ammonia than AOB, allowing them to survive in low oxygen, low ammonium conditions where NOB are not competitive. This pairing of Anammox with AOA would further directly achieve the low effluent ammonia concentrations required to meet effluent targets of the main-line.
Research at Northeastern University: The key to implementation of novel biologically-based treatment strategies lies in development of real-time feedback control strategies that ensure reactor conditions remain stable across fluctuating environmental conditions and continuously favor the target microbial communities. Online sensing and controls that overcome challenging high-interference conditions for traditional sensors will be developed, optimized, and demonstrated on lab scale reactors running the novel granular sludge AOA/Anammox process. High-resolution real-time measurements will take advantage of indirect sensing modes and machine learning data processing techniques to stabilize a micro-aerophilic environment (promotes AOA) at low-concentration ammonia (to maintain ideal conditions and effluent quality).