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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.223

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 head0.032
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