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Record W2329709622 · doi:10.1061/40976(316)172

Stress Effects on Fouling of Flat Sheet Membrane Bioreactor Treating Biodegradable Wastewater

2008· article· en· W2329709622 on OpenAlexaff
Kripa S. Singh, Mi Zhong, Shannon Grant

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2008 · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsEffluentBioreactorFoulingMembrane foulingWastewaterMembrane bioreactorPulp and paper industryMembraneActivated sludgeMixed liquor suspended solidsIndustrial wastewater treatmentMembrane reactorChemistryMaterials scienceChromatographyEnvironmental engineeringEnvironmental science

Abstract

fetched live from OpenAlex

Two laboratory-scale flat sheet membrane bioreactors (FSMBR) with three flat sheet membrane cartridges in each were set up to treat a synthetic biodegradable industrial wastewater. Four stress conditions were employed during the study including different organic loading rates (OLR phase), high salt concentration (salt phase), temperature spikes (temperature phase), and mixed liquor suspended solid (MLSS) concentration (MLSS phase). This study investigated the impact of the stress factors on membrane fouling and evaluated the performance of the FSMBR. Under the imposed stress conditions, the experimental reactor had a smaller particle size, higher average effluent COD concentration, and lower sludge filterability. The experimental reactor also fouled at a faster rate than the control reactor. The results showed that imposed stress conditions had an effect on the bacteria in the MBR which impacted sludge characteristics such as filterability, polysaccharide production, floe size, effluent COD concentration, effluent color, and most importantly the rate of membrane fouling.

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 categoriesInsufficient payload (model declined to judge)
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 score0.184
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
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.191
Teacher spread0.182 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

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