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Record W2002034085 · doi:10.2166/wst.2006.707

Characterization of fouled membranes from a membrane enhanced biological phosphorus removal system

2006· article· en· W2002034085 on OpenAlexafffund
Z. Geng, Eric R. Hall

Bibliographic record

VenueWater Science & Technology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMembraneFoulingMembrane foulingChemistryMembrane bioreactorFiltration (mathematics)Chemical engineeringMicroanalysisChromatographyBiofoulingMembrane technologyOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Characterization of fouled membranes is the first step towards a good understanding of membrane fouling nature and thus formulating effective engineering measures for fouling prevention and control. In this study, fouled membrane fibres collected from a pilot scale membrane enhanced biological phosphorus removal (MEBPR) process were systematically examined. Several analytical tools, including scanning electron microscopy (SEM), conventional optical microscopy (COM), energy dispersive X-ray (EDX) microanalysis, matrix assisted laser desorption/ionization--mass spectrometry (MALDI-MS) analysis, and conventional chemical analysis techniques were used. The results indicated that membrane fouling in the MEBPR process was mainly of an organic nature, and most extractable foulants were carbohydrates and humic or humic-like substances. Unlike in other wastewater treatment membrane bioreactors, microbial growth on fouled membranes was not substantial, probably due to the vigorous aeration applied and the strong hydrodynamic conditions within the membrane pore structure. After a period of sludge filtration, membrane surfaces became more hydrophobic and the resultant hydrophobic interactions between the fouled membranes and mixed liquor constituents might have accelerated the fouling process.

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 categoriesScience and technology studies
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.008
GPT teacher head0.208
Teacher spread0.199 · 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.

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

Citations5
Published2006
Admission routes2
Has abstractyes

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