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Record W1571370882 · doi:10.2175/106143008x325647

Effect of Oxygen Partial Pressure and Chemical Oxygen Demand Loading on the Biofilm Properties in Membrane‐Aerated Bioreactors

2009· article· en· W1571370882 on OpenAlexaff
Ivan X. Zhu, D. Grant Allen, Steven N. Liss

Bibliographic record

VenueWater Environment Research · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of GuelphUniversity of Toronto
Fundersnot available
KeywordsChemical oxygen demandOxygenBiofilmPartial pressureAerationBioreactorChemistryExtracellular polymeric substanceMembraneChemical engineeringBiochemical oxygen demandLimiting oxygen concentrationEnvironmental engineeringWastewaterOrganic chemistryBacteriaBiochemistryEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Membrane-aerated biofilms with oxygen and nutrients diffusing from the opposite sides possess distinct properties, including the ability to couple aerobic and anaerobic processes. The objective of this study was to examine the effects of oxygen partial pressure and chemical oxygen demand (COD) loading on biofilm properties. Two laboratory-scale membrane-aerated bioreactors were operated for a total of 283 days, with one reactor operated at 42, 60, and 89 kPa (0.41, 0.59, and 0.88 atm) oxygen, and the other reactor at 25 kPa (0.25 atm) oxygen (air control). The biofilm detached at the oxygen partial pressures of 60 and 89 kPa (0.59 and 0.88 atm) at a COD loading of 11.3 kg COD/1000 m2/d, but was sustained at the oxygen partial pressures of 25 and 42 kPa (0.25 and 0.41 atm), with a porous structure at the membrane interface at the COD loading of 11.3 kg COD/1000 m2/d. Biofilm formation was improved at a higher COD loading. It is proposed that the loss of extracellular polymeric substances at the biofilm bottom is the cause for the biofilm detachment subjected to a higher oxygen partial pressure.

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 score0.004
Threshold uncertainty score0.708

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 head0.024
GPT teacher head0.252
Teacher spread0.227 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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