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Record W1965719407 · doi:10.1021/ef049744e

Synergetic Role of Polymer Flocculant in Low-Temperature Bitumen Extraction and Tailings Treatment

2005· article· en· W1965719407 on OpenAlexaff
H. Li, Jun Long, Zhenghe Xu, Jacob H. Masliyah

Bibliographic record

VenueEnergy & Fuels · 2005
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTailingsAsphaltOil sandsFlocculationExtraction (chemistry)SettlingPolyacrylamideDewateringPolymerMaterials scienceChemical engineeringPulp and paper industryChemistryEnvironmental scienceChromatographyGeologyEnvironmental engineeringMetallurgyComposite materialGeotechnical engineeringPolymer chemistry

Abstract

fetched live from OpenAlex

This paper presents a preliminary study on the synergetic effect of a polymer flocculant, derived from hydrolyzed polyacrylamide (HPAM), on both bitumen extraction and tailings treatment as applied to oil sands. Bitumen extraction experiments and tailings settling tests were carried out with the addition of HPAM directly in the bitumen extraction process. To understand how the polymer affects bitumen recovery and tailings treatment, the long-range interaction and adhesion forces between bitumen and solids (clay and silica) and between clay and silica were measured using an atomic force microscope (AFM). Our study clearly demonstrated a synergetic role of HPAM in processing poor oil sand ores, which are characterized by high clay fines content. The addition of HPAM at an appropriate dosage not only improved bitumen liberation and recovery but also increased the tailings settling rate. Such improvements were attributed to the selective flocculation of clay fines by HPAM, which was supported by the AFM data. On the basis of the results of the present study, it is suggested to directly add HPAM into the bitumen extraction process, rather than to tailings, to facilitate both bitumen recovery and tailings treatment in production operations.

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 score0.029
Threshold uncertainty score0.553

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 head0.003
GPT teacher head0.200
Teacher spread0.197 · 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

Citations48
Published2005
Admission routes1
Has abstractyes

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