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Record W2463838301 · doi:10.3390/membranes6030038

Impact of PAC Fines in Fouling of Polymeric and Ceramic Low-Pressure Membranes for Drinking Water Treatment

2016· article· en· W2463838301 on OpenAlexaff
Laurent Oligny, Pierre R. Bérubé, Benoît Barbeau

Bibliographic record

VenueMembranes · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsPolytechnique MontréalUniversity of British ColumbiaNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsBackwashingMembraneFoulingMembrane foulingChemical engineeringChemistryChromatographyPowdered activated carbon treatmentBiofoulingCeramicActivated carbonPulp and paper industryOrganic chemistryAdsorption

Abstract

fetched live from OpenAlex

This study assessed the issue of membrane fouling in a Hybrid Membrane Process (HMP) due to the export of powdered activated carbon (PAC) fines from a pretreatment contactor. Two parallel pilot-scale ceramic and polymeric membranes were studied. Reversible and irreversible foulings were measured following three cleaning procedures: Physical backwashing (BW), chemically enhanced backwashing (CEB) and Clean-in-Place (CIP). The impacts on fouling of membrane type, operation flux increase and the presence/absence of the PAC pretreatment were investigated. Membranes without pretreatment were operated in parallel as a control. In addition, CIP washwaters samples were analyzed to measure organic and inorganic foulants removed from the membranes. It was observed that for the polymeric membranes, fouling generally increased with the presence of the PAC pretreatment because of the export of fines. On the contrary, the ceramic membranes were not significantly impacted by their presence. The analysis of CIP washwaters showed a greater total organic carbon (TOC) content on membranes with a PAC pretreatment while no similar conclusion could be made for inorganic foulants.

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 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 score0.051
Threshold uncertainty score0.477

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.262
Teacher spread0.249 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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