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Record W2756091538 · doi:10.1080/09593330.2017.1380710

Effective partial nitrification of ammonia in a fluidized bed bioreactor

2017· article· en· W2756091538 on OpenAlexafffund
Haolong Wang, Mingu Kim, Kai Li, Yuanyuan Shao, Jesse Zhu, George Nakhla

Bibliographic record

VenueEnvironmental Technology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNitrificationBioreactorFluidized bedAmmoniaWaste managementEnvironmental scienceEnvironmental engineeringChemistryPulp and paper industryEnvironmental chemistryNitrogenEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

A lab-scale fluidized bed bioreactor with high-density polyethylene as biofilm carrier media was operated to study partial nitrification (PN) performance with high ammonia concentrations. The system was run at nitrogen loading rates (NLRs) from 1.2 to 4.8 kg N/(m3 d) with empty bed contact time of 2.0 and 2.7 h and four different influent ammonia concentrations of 100, 200, 300 and 400 mg/L. Dissolved oxygen concentration and temperature were maintained around 1.3 mg/L and 35°C, respectively. Stable PN was successfully achieved during the whole period with low effluent NO3-N concentration at less than 15 mg/L, due to effective suppression of nitrite-oxidizing bacteria activity at high concentrations of free ammonia (5.3–27.3 mg N/L) and low alkalinity-to-ammonia ratio. At the NLR of 3.6 kg N/(m3 d), NH4-N conversion and NO2-N accumulation ratios were 57.8% and 53.9%, respectively, which could be further used in the anaerobic ammonium oxidation process (ANAMMOX) as the effluent NO2-N/NH4-N ratio was 1.27.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.220
Teacher spread0.213 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations17
Published2017
Admission routes2
Has abstractyes

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