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Record W4405220065 · doi:10.1021/acsestwater.4c00553

Gravity-Driven Membrane Filtration with Passive Hydraulic Fouling Control for Drinking Water Treatment: Demonstration of Long-Term Performance at Full Scale

2024· article· en· W4405220065 on OpenAlexafffund
Leili Abkar, Binura Senavirathna, Sara E. Beck, William W. Mohn, Matt Seitcher, Pierre R. Bérubé

Bibliographic record

VenueACS ES&T Water · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsNuu Chah Nulth Tribal CouncilUniversity of British Columbia
FundersFirst Nations Health AuthorityNatural Sciences and Engineering Research Council of CanadaIndigenous Services Canada
KeywordsFoulingTurbidityMembrane foulingEnvironmental scienceFiltration (mathematics)BiofilmWater treatmentEnvironmental engineeringChemistryMembraneEcologyBiologyBacteriaMathematics

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide The present study evaluated the performance of a full-scale gravity-driven membrane filtration system with passive hydraulic fouling control (PGDMF) for drinking water treatment in a small community over a 3-year period. The PGDMF system consistently met the design flow and regulated water quality/performance parameters (i.e., total coliform, Escherichia coli, turbidity, and membrane integrity). The instantaneous temperature-corrected permeability (TCP) varied seasonally, being greater during the winter months. The overall TCP decreased slowly to ∼60% of the initial value by the end of 3 years, a TCP that is much greater than would have been expected without passive hydraulic fouling control. Although it was not possible to directly link the observed seasonal changes in TCP to potential seasonal changes in the biofilm microbiome, the analysis did suggest that the lower TCP during summer months was due to a greater microorganism richness in the feed and presence of filamentous, stalked, and biofilm-forming bacteria in the biofilm. Operation with higher trans-membrane pressure (i.e., ∼30 vs ∼20 mbar) and more frequent passive hydraulic fouling control (i.e., every 12 vs 24 h) enabled a greater flow to be sustained. The study demonstrated the long-term robustness and performance of GDMF with passive hydraulic fouling control for drinking 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)0.000
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 score0.019
Threshold uncertainty score0.487

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 communication0.0000.001
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 head0.010
GPT teacher head0.226
Teacher spread0.215 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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