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Record W2149465900 · doi:10.1002/aic.11649

Effects of sand and process water pH on toluene diluted heavy oil in water emulsions in turbulent flow

2008· article· en· W2149465900 on OpenAlexafffundabout
Chandra W. Angle, Hassan Hamza

Bibliographic record

VenueAIChE Journal · 2008
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsDevon Energy (Canada)Natural Resources Canada
FundersGovernment of Canada
KeywordsCoalescence (physics)EmulsionMixing (physics)Oil dropletOil sandsBreakageChemistryChemical engineeringWater flowMaterials scienceEnvironmental engineeringEnvironmental scienceComposite materialOrganic chemistryAsphalt

Abstract

fetched live from OpenAlex

Abstract The presence of sand in heavy oil production is known to enhance oil recovery. Sand can also be detrimental depending on the properties of the sand–water interface. In this process, the water soluble material interacts with both sand and oil droplets and affects emulsion stability. The formation and stability of heavy oil‐in‐water emulsions during turbulent flow using batch process stirred‐tank mixing of oil, sand, and water were investigated at three pH. Size distributions were measured by laser diffraction. High‐speed video photomicrography was used to observe the process during mixing. Results showed that the presence of sand enhanced formation of stable, fine emulsion at basic pH 8.5. When the pH of the water was reduced below 6.5 both sand and droplets surface properties changed, the emulsions became less stable and coalescence was apparent. The sand grains acted as coalescers at low pH and enhanced breakage at high pH. © Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2008 AIChE J, 2009

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 score0.018
Threshold uncertainty score0.364

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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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

Citations12
Published2008
Admission routes3
Has abstractyes

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