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Record W2991164290 · doi:10.2166/wqrj.2019.009

Impact of mixing intensity on dissolved oxygen half-velocity constants in a sidestream deammonification environment

2019· article· en· W2991164290 on OpenAlexafffund
Biao Xie, Chao Jin, Wayne J. Parker

Bibliographic record

VenueWater Quality Research Journal · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnammoxMixing (physics)Oxidizing agentChemistryAmmoniaNitrogenAmmoniumOxygenIntensity (physics)Analytical Chemistry (journal)Anaerobic exerciseEnvironmental chemistryDenitrificationBiochemistryBiology

Abstract

fetched live from OpenAlex

Abstract A partial nitritation/anammox (PN/A) process was operated at two different mixing intensities to quantify the extent to which diffusional limitations impact process rates. At a steady-state operation, the total inorganic nitrogen removal efficiency in the bench-scale sequencing batch reactors was found to increase as mixing intensity decreased (62 and 84% for average velocity gradient (G) values of 15 and 5.3 s−1, respectively). The half-velocity constants with respect to bulk-phase dissolved oxygen (DO) concentration for ammonia-oxidizing bacteria (AOB) and anaerobic ammonium-oxidizing (anammox) organisms were estimated on the basis of nitrogen removal rates that were observed in activity tests. The activity tests were conducted over a range of bulk-phase DO concentrations. The best-fit values were estimated to be 0.68 ± 0.34 and 0.54 ± 0.56 mg O2/L for G values of 15 and 5.3 s−1, respectively. The AOB values were not statistically different (p = 0.19) between mixing conditions which were consistent with AOB dominating the surface of granules. The best-fit values were estimated to be 0.13 ± 0.09 and 0.55 ± 0.40 mg O2/L for G values of 15 and 5.3 s−1, respectively, and were statistically different . The results demonstrated that mixing conditions should be considered when designing PN/A processes and provide quantitative results that can be employed to improve models of these processes. This article has been made Open Access thanks to the kind support of CAWQ/ACQE (https://www.cawq.ca).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.079
GPT teacher head0.360
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2019
Admission routes2
Has abstractyes

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