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Record W4379739956 · doi:10.1002/cjce.25012

Using polymer adsorption data and a population balance model to estimate how polymer dosage affects the flocculation of oil sands tailings

2023· article· en· W4379739956 on OpenAlexafffundvenue
Daniel V. Dixon, João B. P. Soares

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicCoagulation and Flocculation Studies
Canadian institutionsUniversity of Alberta
FundersInstitute for Oil Sands Innovation, University of AlbertaNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsFlocculationTailingsPolymerPopulationAdsorptionOil sandsChemical engineeringPolymer adsorptionChemistryChromatographyMaterials scienceComposite materialMetallurgyOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Abstract Population balance models can describe how particles aggregate and fragment during the flocculation of mineral tailings. We used a new method to estimate some of the parameters in a population balance model describing the flocculation of oil sands mature fine tailings with poly(vinyl benzyl trimethylammonium chloride). Differently from previous population balance models, the polymer adsorption data onto the particles suspended in the tailings were used to estimate a fundamental parameter relating polymer dosage to the mean diameter of the aggregates formed during flocculation. The model could predict the flocculation behaviour of three polymer samples with different molecular weights. This model is another step toward a quantitative understanding of how polymer properties affect the flocculation of mineral tailings.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.032
GPT teacher head0.263
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations3
Published2023
Admission routes3
Has abstractyes

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