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Record W4316923292 · doi:10.3808/jeil.202200092

Strategies for Mitigating MBR Membrane Biofouling

2022· article· en· W4316923292 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Environmental Informatics Letters · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsBiofoulingExtracellular polymeric substanceMembraneMembrane bioreactorWastewaterReuseMembrane foulingSewage treatmentChemistryBioreactorPulp and paper industryBiochemical engineeringEnvironmental scienceEnvironmental engineeringWaste managementFoulingBacteriaBiofilmEngineeringBiologyBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Membrane biofouling is a roadblock to the application of membrane bioreactors (MBR) for wastewater treatment and reuse. Strategies for the mitigation of membrane biofouling have been extensively reviewed in this paper. The review was focused on feedwater pretreatment, modified membranes, suppression of the secretion and discharge of extracellular polymeric substances (EPSs) and soluble microbial products (SMPs), and novel MRB systems. The three identified novel strategies for mitigating membrane biofouling in an MBR for wastewater reclamation are lower EPS concentration by adding D-amino acids (D-AAs) and a cationic polymer flucculant; introducing a modified membrane in an MBR; and applying a novel algae-bacteria system. Experimental results have shown that membrane biofouling has been mitigated to some extent via these strategies.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.999

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)0.0020.000

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 head0.012
GPT teacher head0.225
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