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Record W2896684188 · doi:10.1051/e3sconf/20185900001

Membrane surface morphology and fouling in filtration of high DOC water

2018· article· en· W2896684188 on OpenAlexafffundabout
Beata Gorczyca

Bibliographic record

VenueE3S Web of Conferences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsNanofiltrationUltrafiltration (renal)FoulingMicrofiltrationMembraneChemistryFiltration (mathematics)Dissolved organic carbonMembrane foulingChlorineSurface waterChromatographyEnvironmental chemistryChemical engineeringEnvironmental engineeringEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

In Canada many potable water sources contain very high concentrations of Dissolved Organic Carbon (DOC), accompanied by a wide range of hardness. DOC reacts with chlorine used in water disinfection to form potentially carcinogenic chlorine disinfection by-products – Trihalomethanes (THMs) and haloacetic acids (HAAs). Dual membrane plants that combine microfiltration (MF) and ultrafiltration (UF)or nanofiltration (NF) can remove DOC and reduce THMs concentration, but these plants are prone to serious fouling of their UF or NF membranes. The objectives of our research are to study the mechanisms of UF/NF membrane fouling. We have determined various resistances of DOW Filmtech NF90 (flat sheet coupon), based on the resistance in series model. The experiments were conducted on a bench scale cross-flow membrane filtration unit (Sterlitech), using synthetic water with DOC of 11 mg/L and calcium hardness of 350 mg/L that represents typical surface waters in Manitoba (Canada). The results suggest that gel layer on the surface of the membrane has a significant contribution to the flux decline. Atomic Force Microscope (AFM) allowed for relatively inexpensive, non-destructive analysis of the surface area of the gel layer deposited on the membrane filter. The morphology of the gel layer was related to the gel layer resistance.

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 score0.029
Threshold uncertainty score0.998

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)0.0030.000

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 head0.019
GPT teacher head0.247
Teacher spread0.228 · 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

Citations0
Published2018
Admission routes3
Has abstractyes

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