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Record W2067520506 · doi:10.2166/wst.2007.644

Comparison of mixed liquor filterability measured with bench and pilot-scale membrane bioreactors

2007· article· en· W2067520506 on OpenAlexaff
John-Paul Nywening, Haibo Zhou, Hadi Husain

Bibliographic record

VenueWater Science & Technology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFoulingFiltration (mathematics)BioreactorMembrane bioreactorMembrane foulingFull scalePulp and paper industryActivated sludgeChemistryMembraneWastewaterWaste managementEnvironmental scienceEnvironmental engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

Parallel experimental tests to measure mixed liquor filterability for submerged membrane bioreactors were conducted over a six month period using three ZW-500 pilot plants and a ZW-10 lab-scale filterability apparatus. Non-air sparged conditions during the tests yielded operation behaviour that was equivalent to dead-end filtration. The fouling resistance increased linearly with the intercepted mass until a critical point was reached at which point significant cake compression was induced and the resistance began to increase exponentially. Although the point of cake compression appears to be dependent on the membrane module design, similar resistance per unit solid mass intercepted per unit area (R(mass)) values were observed when the same mixed liquor was filtered. Coupled with the established correlation between the R(mass) and the critical flux, it is suggested that the filterability test results from a side-stream, lab-scale module may be used to predict fouling potential in a full scale MBR wastewater treatment system without interrupting the full-scale MBR operation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.273
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2007
Admission routes1
Has abstractyes

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