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Record W2044097276 · doi:10.1061/40927(243)271

Effect of Soluble Microbial Products on Simultaneous Nitrification-Denitrification in MBRs

2007· article· en· W2044097276 on OpenAlexafffund
Ladan Holakoo, George Nakhla, Ernest K. Yanful, Amarjeet Bassi

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2007 · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnoxic watersChemistryNitrificationSimultaneous nitrification-denitrificationDenitrificationNitrogenPulp and paper industryCompartmentalization (fire protection)BioreactorEnvironmental engineeringEnvironmental chemistryEnvironmental scienceEnzymeBiochemistry

Abstract

fetched live from OpenAlex

Previous work on MBR reported nitrogen removal via simultaneous nitrification and denitrification (SND) to be unstable despite steady state conditions and the concentrations of soluble microbial products (SMP) in MBR were found to vary up to 60% of the average value. The results of the present study showed that oxygen transfer efficiency (KLa) is a function of the mixed liquor suspended solids and SMP concentrations with improved KLa at lower biomass and higher SMP concentrations. Under limited DO conditions, the reaction rate inside the floc changes with KLa and while nitrification is enhanced at higher KLa SND decreases. Therefore, in order to achieve a reliable and stable SND in MBRs, a very tight DO control or other means of DO control such as control based on the activity of anoxic enzyme could be a more viable option. This might necessitate spatial arrangement of membranes or compartmentalization of submerged MBR tanks for a better DO control.

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)0.001
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 score0.171
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.196
Teacher spread0.192 · 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

Citations0
Published2007
Admission routes2
Has abstractyes

Explore more

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