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Record W2158335751 · doi:10.1002/cjce.20283

Polymer aids for settling and filtration of oil sands tailings

2010· article· en· W2158335751 on OpenAlexafffundvenue
Xiaoyan Wang, Xianhua Feng, Zhenghe Xu, Jacob H. Masliyah

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTailingsOil sandsFlocculationSettlingFiltration (mathematics)Waste managementPolymerMoistureEnvironmental sciencePulp and paper industryMaterials scienceEnvironmental engineeringMetallurgyComposite materialAsphaltEngineeringMathematics

Abstract

fetched live from OpenAlex

Abstract Commercial Magnafloc 1011 (Percol 727) polymer and in‐house synthesised Al‐PAM polymer were used to flocculate oil sands tailings that were derived from low and high fines oil sands ores. Fines are defined as mineral solids less than 44 µm. The performance of polymers was evaluated in terms of tailings settling, filtration rate, and final moisture content of tilter cakes. Both polymers were shown to effectively flocculate the derived oil sands tailings and hence to enhance tailings settling. Al‐PAM performed very well as a filtration aid. The final moisture content of the filter cake obtained from tailings derived from the low fines ore was 6.6 ± 1.2 wt.% and that from the high fines ore was 16.9 ± 0.8 wt.%. This class of polymer can provide an alternative approach for oil sands tailings disposal that has the potential to eliminate tailings ponds. However, the commercial Magnafloc 1011 polymer was found ineffective as a filtration aid for the two tailings tested in this study.

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 score0.011
Threshold uncertainty score0.299

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 head0.004
GPT teacher head0.183
Teacher spread0.179 · 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

Citations70
Published2010
Admission routes3
Has abstractyes

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