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Record W3021717786 · doi:10.1002/cjce.23777

Intermittent ultrasound‐assisted ceramic membrane fouling control in ultrafiltration

2020· article· en· W3021717786 on OpenAlexaffvenue
Kyu Min Lee, Huu Doan, Farhad Ein‐Mozaffari

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsUltrasoundUltrasonic sensorMaterials scienceFoulingUltrafiltration (renal)Ceramic membraneMembrane foulingCavitationCeramicMembraneVolumetric flow rateChromatographyMicrofiltrationCross-flow filtrationBiomedical engineeringChemistryComposite materialAcousticsMedicine

Abstract

fetched live from OpenAlex

Abstract In this study, remediation of ceramic membrane fouling by an in‐line intermittent ultrasound system was investigated. A piezoelectric ultrasonic transducer was integrated into a membrane unit that provided ultrafiltration (UF) of a diluted skim milk solution containing 0.10 wt% of protein. The effects of ultrasound at varied frequencies (20, 28, and 40 kHz) and power intensities (1.44, 2.88, and 5.76 W/cm 2 ) under continuous operation and intermittent mode at various intervals (0.50, 1.0, 1.5, 2.0, 2.5, and 3.0 minutes) on membrane fouling were studied. The quality and flow rate of the permeate stream were monitored for the evaluation of the UF process performance. Optimal conditions of continuous ultrasound were found at 28 kHz and 2.88 W/cm 2 . Moreover, at optimal ultrasonic conditions, the optimal intermittent time was found at 0.50 minute. At optimal ultrasonic conditions, the permeate amount increased by 79.8% and 94.2% for 0.50 minute intermittent ultrasound and continuous ultrasound, respectively, as compared with that of the UF process without ultrasound. Also, intermittent ultrasound induced better fouling control at a lower protein concentration of 0.05% by weight. The cleaning effect of ultrasound could be attributed to the cavitation bubbles generated by the rarefaction and pressure cycles of the applied ultrasound.

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)0.001
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 score0.011
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.191
Teacher spread0.180 · 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

Citations18
Published2020
Admission routes2
Has abstractyes

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