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Record W2039653229 · doi:10.1139/s07-028

Coagulation assisted membrane filtration to treat high strength wastewater from municipal solid waste anaerobic digesters

2008· article· en· W2039653229 on OpenAlexafffundvenue
Xin Xie, Hongde Zhou, Calvin Chong, Bruce E. Holbein

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsDiscovery Air (Canada)University of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryFiltration (mathematics)AerationEffluentMembrane foulingUltrafiltration (renal)Pulp and paper industryFoulingOrganic matterWastewaterCoagulationAlumAnaerobic digestionSuspended solidsChromatographyMembraneEnvironmental chemistryEnvironmental engineeringEnvironmental scienceMethane

Abstract

fetched live from OpenAlex

Effluents from the anaerobic digestion of municipal solid wastes contain a variety of organic and inorganic components with a high concentration of nitrogen and phosphorous. The feasibility to treat these effluents prior to discharge or reuse was evaluated experimentally using membrane filtration coupled with coagulation pretreatment. Standard jar tests were conducted by varying alum doses and pH values. Both raw and coagulated wastewaters were then filtered using a submerged ultrafiltration apparatus at different sparging aeration rates and permeate fluxes. It was shown that coagulation reduced the membrane fouling rates due to the increase in particle sizes. Furthermore, the flocs formed from sweep coagulation had a better filterability than those from charge neutralization. However, coagulation pretreatment only moderately increased the removal efficiencies of organic matter from subsequent ultrafiltration because only a portion of oganic matter could be adsorbed and (or) co-precipitated on coagulated flocs.

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 score0.231
Threshold uncertainty score0.526

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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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

Citations5
Published2008
Admission routes3
Has abstractyes

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