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Record W4226189308 · doi:10.5004/dwt.2022.27994

Comparison of dissolved air flotation and ballasted sedimentation for the treatment of an algal impacted water

2022· article· en· W4226189308 on OpenAlexaff
Juan P. González-Galvis, Richard M. Hérard, Elise Berthier, Roberto Narbaitz

Bibliographic record

VenueDesalination and Water Treatment · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSedimentationEnvironmental scienceDissolved air flotationWater treatmentEnvironmental engineeringGeologySedimentSewage treatmentGeomorphology

Abstract

fetched live from OpenAlex

ABSTRACT Dissolved air flotation (DAF) is considered better than conventional gravity settling (CGS) for the treatment of algal-laden waters, and ballasted sedimentation (BS), a high-rate separation process, is now often used instead of CGS. Our initial literature search did not identify DAF-BS comparisons for the removal of algae and cyanobacteria from algal laden waters. The objective of this bench-scale study was to compare DAF with BS and CGS for the treatment of water from a eutrophic waterway (Bay of Quinte, ON). The study was performed mid-summer when there was substantial algal growth. The optimized BS jar tests had 3% lower average turbidity removal than the DAF jar tests, however BS required 33% more coagulant, as well as 0.25 mg/L anionic polymer and microsand additions. The removal of cyanobacteria and algae (quantified using chlorophyll-a and c-phycocyanin concentrations) by DAF and BS were very similar, and they were superior to that achieved by CGS. The DOC removals and the disinfection by-products formation potential (DBPFP) of DAF and BS treated water were also similar. Based on the chlorophyll-a and c-phycocyanin removals, both BS and DAF performed better than CGS and can be considered suitable for the treatment of algal/cyanobacteria laden waters.

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 score0.074
Threshold uncertainty score0.295

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 communication0.0000.000
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 head0.028
GPT teacher head0.325
Teacher spread0.297 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

Explore more

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