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Record W2426096264 · doi:10.2175/106143007x194329

Use of Chemical Coagulants to Control Fouling Potential for Wastewater Membrane Bioreactor Processes

2007· article· en· W2426096264 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWater Environment Research · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMembrane foulingFoulingMembrane bioreactorChemistryUltrafiltration (renal)Filtration (mathematics)MembraneAlumWastewaterCoagulationChromatographyBioreactorFerricChemical oxygen demandFlocculationPulp and paper industryChloridePermeationMembrane technologyChemical engineeringEnvironmental engineeringInorganic chemistryEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Chemical coagulation with ferric chloride, alum, and an organic polymer were used to control the fouling potential of mixed liquors for submerged membrane bioreactor (MBR) processes in treating municipal wastewater. Their filterability was evaluated using a submerged hollow fiber ultrafiltration apparatus operated in constant permeate flux mode. The collected transmembrane pressures over filtration time were used to calculate the membrane fouling rates. The results showed that coagulation pretreatment can reduce fouling rates when MBRs were operated above the critical flux. Even though coagulation with the organic polymer formed larger mixed liquor suspended solids particles and had shorter time-to-filtration than those with ferric chloride and alum, the filterability for membrane filtration were similar, indicating that the membrane fouling in MBR systems was mainly controlled by the concentration of smaller colloidal particles.

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.

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 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 score0.008
Threshold uncertainty score1.000

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)0.0010.001

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 head0.065
GPT teacher head0.317
Teacher spread0.252 · 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