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Hydrodynamic Treatment of Wastewater Effluent Flocs for Improved Disinfection

2012· article· en· W2410295037 on OpenAlexafffund
John Gibson, Ian G. Droppo, Ramin Farnood, B. Mahendran, Peter Seto, Steven N. Liss

Bibliographic record

VenueWater Environment Research · 2012
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsQueen's UniversityUniversity of GuelphUniversity of TorontoEnvironment and Climate Change Canada
FundersCanadian Water Network
KeywordsBreakageEffluentWastewaterFlocculationCavitationParticle (ecology)Sewage treatmentEnvironmental engineeringWater treatmentChemistryPulp and paper industryEnvironmental scienceMaterials scienceComposite materialGeologyMechanics

Abstract

fetched live from OpenAlex

Hydrodynamic forces generated by an orifice plate under low pressure were examined as a means of disrupting flocs, in order to improve disinfection of treated wastewater effluents. Changes in cavitation conditions were found to have little impact on the extent of particle breakage in this experimental setup. The rate of strain (flow rate divided by the hole radius cubed), however, was found to be the best predictor of floc breakage. Floc breakage was not affected by changes in floc concentration, but was very sensitive to differences between flocs collected from different sources. Larger flocs (90 to 106 microm) were broken apart to a greater extent than smaller ones (53 to 63 microm). Hydrodynamic treatment decreased the viability of bacteria associated with large flocs, and also increased the ultraviolet dose response by up to one log unit (i.e., a factor of ten). Subjecting final effluent wastewaters to hydrodynamic treatment, therefore, provides a treatment strategy for conditions in which the presence of flocs limits the level of disinfection that can be achieved.

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)0.001
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 score0.974

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.037
GPT teacher head0.308
Teacher spread0.271 · 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

Citations7
Published2012
Admission routes2
Has abstractyes

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