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Record W2338181669 · doi:10.2166/wqrjc.2015.011

Membrane fouling remediation in ultrafiltration of latex contaminated wastewater

2015· article· en· W2338181669 on OpenAlexafffund
Amira Abdelrasoul, Huu Doan, Ali Lohi

Bibliographic record

VenueWater Quality Research Journal · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryFoulingMembraneUltrafiltration (renal)EffluentIonic strengthMembrane foulingZeta potentialPolyvinylidene fluorideChromatographyChemical engineeringAqueous solutionEnvironmental engineeringNanoparticleOrganic chemistry

Abstract

fetched live from OpenAlex

The current study aimed to remediate membrane fouling by latex effluent by altering membrane surface charge or ionic strength of the effluent. Hydrophilic polysulfone and Ultrafilic flat membranes, with molecular weight cutoff (MWCO) of 60,000 and 100,000, respectively, and hydrophobic polyvinylidene difluoride membrane (PVDF; MWCO 100,000) were used under a constant flow rate and in cross-flow mode for ultrafiltration of latex effluent. The effect of linear alkyl benzene sulfonate (LAS) on the ionic strength of the effluent and the zeta potential of latex particles was investigated. LAS was also used to improve the anti-fouling properties of the membrane surface. The ionic strength of latex effluent was increased by raising its pH from 7 to 12, resulting in an increase of the zeta potential negativity of the latex particles from −26.61 to −42.66 mV. LAS was found to be an ineffective pretreatment for limiting the fouling propensity of latex effluent using hydrophilic membranes even at high concentration and long treatment times. It was concluded that LAS-treated membrane surface is much more favorable than pH changed feed pretreatment. The total mass of fouling decreased by 44.00 and 29.60%, when PVDF membrane surface was treated with LAS at a concentration of 1 × 10−4 g/L, and latex effluent at pH 11 was used, respectively.

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)0.011
metaresearch head score (Gemma)0.001
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 score0.016
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.176
GPT teacher head0.401
Teacher spread0.225 · 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

Citations12
Published2015
Admission routes2
Has abstractyes

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