MétaCan
Menu
Back to cohort
Record W2034236214 · doi:10.2166/wst.2010.111

Effects of powdered activated carbon dosing on sludge characteristics and estrogen removal in membrane bioreactors

2010· article· en· W2034236214 on OpenAlexaff
W. Yang, Michelle Paetkau

Bibliographic record

VenueWater Science & Technology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsChemistryMembrane bioreactorExtracellular polymeric substanceMembrane foulingPowdered activated carbon treatmentDosingChromatographyBioreactorFiltration (mathematics)Activated sludgeMembraneFoulingActivated carbonSewage treatmentBiochemistryEnvironmental engineeringOrganic chemistryBiologyBacteriaEnvironmental scienceAdsorption

Abstract

fetched live from OpenAlex

Sludge characteristics associated with filterability as well as the removal of a natural estrogen 17beta-estradiol (E2) and a synthetic estrogen 17alpha-ethyinylestradiol (EE2) were investigated in submerged membrane bioreactors (MBR) with and without the addition of powdered activated carbon (PAC) under the same experimental conditions. Positive impacts of PAC dosing on membrane fouling and the removal of E2 and EE2 were demonstrated over a six months stable operational period. Experimental results showed that PAC dosing resulted in lower concentrations of soluble extracellular polymeric substances (EPS) and colloidal total organic carbon (TOC) in the PAC-MBR sludge. The average soluble EPS and colloidal TOC concentrations in the PAC-MBR sludge was 60.1% and 61.8% lower than the control MBR sludge, respectively. Regardless of PAC dosing, concentrations of colloidal TOC were strongly related to concentrations of soluble EPS and soluble carbohydrates in the sludge. In addition, the mean flocs size of the sludge with PAC dosing was shifted from 49.4 microm to 60.3 microm. PAC dosing in the MBR increased the removal rates of E2 and EE2 by 3.4% and 15.8%, respectively.

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 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 score0.009
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.218
Teacher spread0.212 · 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

Citations11
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueWater Science & TechnologySame topicMembrane Separation TechnologiesFrench-language works237,207