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Evaluation of Apparatus for Membrane Cleaning Tests

2010· article· en· W2165431192 on OpenAlexafffund
Đặng Thị Thanh Huyền, Roberto Narbaitz, Takeshi Matsuura

Bibliographic record

VenueJournal of Environmental Engineering · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUltrafiltration (renal)FoulingMembraneFiltration (mathematics)Flux (metallurgy)Membrane foulingChemistryMembrane technologyMicrofiltrationWater treatmentChemical engineeringEnvironmental engineeringChromatographyMaterials scienceEnvironmental scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

Membrane cleaning is critical to the operation of membrane processes. This paper studies the impact of using four different types of bench-scale membrane systems to assess the effectiveness of different cleaning steps after the filtration of colored river water. The systems are a stirred ultrafiltration (UF) cell, a SEPA cell, a small cross-flow (CF) cell, and a six-CF-cell-in-parallel system. The effect of cleaning frequency was also investigated. The comparison was implemented in terms of flux recovery, solute removal, solute resistance removal, and changes of contact angles. The stirred UF cell was only reliable and comparable in terms of flux and flux recovery results. The six-cell-in-parallel system requires further development due to their much lower flux. For cleaning at 30-min intervals, the cleaning efficiency of membranes was similar for the three CF systems. For cleaning intervals of 2 and 4 h did not statistically affect the flux recovery for the stirred UF cell and SEPA cell. There was some irreversible fouling that could not be restored completely by clean-in-place method even with rigorous chemical treatment.

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)0.001
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 score0.046
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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

Citations8
Published2010
Admission routes2
Has abstractyes

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