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Record W3081972806 · doi:10.3390/membranes10090213

Ozone Chemically Enhanced Backwash for Ceramic Membrane Fouling Control in Cyanobacteria-Laden Water

2020· article· en· W3081972806 on OpenAlexafffund
Stéphane Venne, Onita D. Basu, Benoît Barbeau

Bibliographic record

VenueMembranes · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsPolytechnique MontréalCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMembrane foulingOzoneFoulingFiltration (mathematics)CyanobacteriaUltrafiltration (renal)Ceramic membraneWater treatmentMembraneChemistryPulp and paper industryEnvironmental engineeringChemical engineeringEnvironmental scienceChromatographyBacteriaGeology

Abstract

fetched live from OpenAlex

Membrane fouling in surface waters impacted by cyanobacteria is currently poorly controlled and results in high operating costs. A chemically enhanced backwash (CEB) is one possible strategy to mitigate cyanobacteria fouling. This research investigates the potential of using an ozone CEB to control the fouling caused by Microcystis aeruginosa in filtered surface water on a ceramic ultrafiltration membrane. Batch ozonation tests and dead-end, continuous flow experiments were conducted with ozone doses between 0 and 19 mg O3/mg carbon. In all tests, the ozone was shown to react more rapidly with the filtered surface water foulants than with cyanobacteria. In addition, the ozone CEB demonstrated an improved mitigation of irreversible fouling over 2 cycles versus a single CEB cycle; indicating that the ozone CEB functioned better as the cake layer developed. Ozone likely weakens the compressible cake layer formed by cyanobacteria on the membrane surface during filtration, which then becomes more hydraulically reversible. In fact, the ozone CEB reduced the fouling resistance by 35% more than the hydraulic backwash when the cake was more compressed.

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 categoriesInsufficient payload (model declined to judge)
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 score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.227
Teacher spread0.213 · 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; both teacher heads agree on what is shown here.

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

Citations11
Published2020
Admission routes2
Has abstractyes

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