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Record W2913546157 · doi:10.5004/dwt.2019.23423

Investigation into the temperature effect on NOM fouling and cleaning in submerged polymeric membrane systems

2019· article· en· W2913546157 on OpenAlexaff
Mohammad T. Alresheedi, Onita D. Basu

Bibliographic record

VenueDesalination and Water Treatment · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsFoulingMembrane foulingMembraneBiofoulingChemical engineeringPolymeric membraneMaterials scienceChemistryEngineering

Abstract

fetched live from OpenAlex

ABSTRACT Membrane fouling is one of the main factors that hinders the wide application of ultrafiltration (UF) processes. Limited research on the influence of temperature condition on reversible and irreversible natural organic matter (NOM) fouling has been conducted. Fouling and cleaning of a submerged polymeric UF with different NOM components were examined at 5°C, 20°C, and 35°C. Fouling was evaluated using the modified fouling index-UF (MFI-UF) and unified membrane fouling index (UMFI) indices, analysis of cake layer properties, and specific flux recovery. Results showed that fouling increased by 15%–35% when water temperature decreased from 20°C to 5°C, whereas fouling decreased by 15%–25% when the temperature increased to 35°C. The UMFI f fouling order was consistent across all temperature conditions with the NOM mixture and bovine serum albumin (BSA) fouling more severely than the alginate and humic acid. The UMFI and MFI-UF exhibited the same fouling order and can be used in complement to each other. BSA was found to be more sensitive to temperature changes and irreversibly fouling more than humic acid and alginate. The ratio of irreversible to reversible fouling (UMFI hir /UMFI hr ) increased by 15%–25% from 35°C to 20°C and by 30%– 40% from 20°C to 5°C indicating the need for altered cleaning strategies at cold water conditions.

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 score0.108
Threshold uncertainty score0.253

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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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