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Effect of Sodium Citrate on the Aggregation of Bitumen Droplets

2022· article· en· W4221078386 on OpenAlexafffund
Xu Zhang, Bo Liu, James S. Grundy, Rogério Manica, Qingxia Liu

Bibliographic record

VenueEnergy & Fuels · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaSyncrude
KeywordsAsphaltOil dropletOil sandsChemistryChemical engineeringExtraction (chemistry)Particle sizeMixing (physics)CoatingMineralogyChromatographyEmulsionMaterials scienceComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

The bitumen droplet size is a key factor affecting recovery in mined oil sand extraction. In this study, we investigate the impact of sodium citrate (Na3Cit), a secondary processing aid, on the bitumen droplet size after the oil sand liberation process. By developing a model mixing system, we found that Na3Cit facilitates the aggregation of midsized droplets of ∼50–100 μm to form larger droplet flocs of ∼500–600 μm. To unveil the underlying mechanism, we further studied the size evolution of emulsified bitumen droplets under different water chemistries using focused beam reflectance measurement and smart online particle analysis technology. The results further confirmed that Na3Cit can facilitate droplet aggregation; typically, an optimum dosage could be identified, above which the beneficial effect on aggregation started to decrease and eventually became detrimental. The impact of Na3Cit on the bitumen droplet size may be attributed to the interplay of three major factors: slime coating, surface properties, and surface forces under different water chemistries. This research indicates that larger bitumen flocs can be formed at an optimum Na3Cit concentration, which in turn results in a higher bitumen flotation and increased bitumen recovery.

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 categoriesInsufficient payload (model declined to judge)
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 score0.023
Threshold uncertainty score0.997

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)0.0040.000

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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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.

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

Citations3
Published2022
Admission routes2
Has abstractyes

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