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- W2955187002 abstract "The project aims at to enable nitrogen removal using an Anammox granular sludge technique to mainstream wastewater treatment, which could result in reducing treatment aeration costs by 50%, space requirements by 75% and operating costs by 30% compared to conventional treatment processes. Anammox technology is currently effectively applied only in the side-stream because mainstream wastewater has low temperature and low concentration of ammonium, conditions where Nitrite Oxidizing Bacteria (NOB) competes with Anammox bacteria for NO2-. Ammonium-oxidizing Archaea (AOA) were found to have high affinity for NH4+, much higher than ammonia-oxidizing bacteria (AOB), and therefore could be able to successfully outcompete NOB in mainstream conditions. The Winkler Lab at the University of Washington is combining AOA with Anammox bacteria to enable Anammox processes in the mainstream, however reactor stabilization at target mainstream conditions requires innovation in online sensing. This thesis lays the groundwork for development of a sensing strategy to maintain the reactor condition optimal for growth of the target microbial (AOA/Anammox) community (ammonia concentrations down to 10µM). This requires understanding reactor chemistry and response patterns for commercial sensors under these conditions. Samples were taken from the test reactor and were measured for concentration of Na+, K+, Mg2+, Ca2+ using atomic absorption spectroscopy. The concentrations were evaluated on the basis of their interdependence and their statistical distributions. Using these trends a set of 50 synthetic samples was generated to be analyzed using ion selective electrode sensors (nominally responsive to Na+, K+, Ca2+, NH4+, Cl- and NO3-) which are also installed in the UW lab reactor . Ammonium concentrations were uniformly distributed between 10-40µM (target operating conditions). The synthetic samples were also measured for their pH and conductivity. It was determined that inclusion of carbonates in the creation of synthetic samples (rather than assuming atmospheric equilibration) was critical for achieving target pH. Outputs of these analyses provide a training dataset for use in machine learning approaches for online sensor design." @default.
- W2955187002 created "2019-07-12" @default.
- W2955187002 creator A5014085066 @default.
- W2955187002 date "2021-05-10" @default.
- W2955187002 modified "2023-10-03" @default.
- W2955187002 title "Characterization of reactor composition for development of in-situ sensing technology for a novel biological process" @default.
- W2955187002 doi "https://doi.org/10.17760/d20316354" @default.
- W2955187002 hasPublicationYear "2021" @default.
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