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Record W2136637305 · doi:10.1139/s02-006

Performance comparison and pretreatment evaluation of three water treatment membrane pilot plants treating low turbidity water

2002· article· en· W2136637305 on OpenAlexafffundvenue
Khosrow Farahbakhsh, Daniel W. Smith

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Alberta
FundersUniversity of British Columbia
KeywordsMicrofiltrationMembraneMembrane foulingUltrafiltration (renal)TurbidityFoulingPowdered activated carbon treatmentWater treatmentCoagulationChromatographyChemistryPulp and paper industryChemical engineeringActivated carbonEnvironmental engineeringEnvironmental scienceAdsorptionBiochemistryOrganic chemistryBiologyEcology

Abstract

fetched live from OpenAlex

The performance of three different low-pressure water treatment membrane pilot plants was evaluated on a low-turbidity, low-colour water in British Columbia. All membrane units were hollow-fibre, hydrophilic membranes with nominal pore sizes of 0.01 to 0.2 µm. In addition to finished water quality, specific flux of each membrane was used to compare the performance of the three membrane units. The effect of feed pretreatment on the performance of the membranes and removal of disinfection byproduct (DBP) precursors was evaluated using chemical coagulation and powdered activated carbon (PAC) addition. Chemical coagulation proved to be very effective in removing DPB precursors and reducing the rate of membrane fouling. Powdered activated carbon addition, on the other hand, resulted in moderate removal of DBP precursors but increased the rate of membrane fouling. The nature and causes of membrane fouling as well as the impact of pretreatment methods on membrane fouling are discussed. Key words: ultrafiltration, microfiltration, coagulation, PAC, fouling, hollow fibre membranes, pretreatment, water 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 score0.206
Threshold uncertainty score0.438

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 communication0.0000.001
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 head0.032
GPT teacher head0.227
Teacher spread0.195 · 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

Citations24
Published2002
Admission routes3
Has abstractyes

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