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Record W4376565476 · doi:10.2118/213017-ms

Liquid-Liquid Equilibrium Studies of Carbon Dioxide/Bitumen System and Utilizing Ethyl Acetate as a Co-Solvent

2023· article· en· W4376565476 on OpenAlexaff
Mohammad S. Khan, Hassan Hassanzadeh

Bibliographic record

VenueSPE Western Regional Meeting · 2023
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSolubilityAsphaltViscosityEthyl acetateSolventChemistryHydrocarbonExtraction (chemistry)Mixing (physics)Chemical engineeringMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In situ bitumen extraction from oil sands using thermal recovery processes has faced challenges due to reliance on steam. Additionally, the produced bitumen is highly viscous and needs to be diluted with lighter hydrocarbon products, such as field condensates, for pipeline transportation. Therefore, exploring less energy-intensive options to produce and transport bitumen economically with less environmental impact is essential. This work aimed to study the liquid-liquid equilibrium (LLE) of CO2 and bitumen at ambient temperature. First, the impact of CO2 feed mass fraction and pressure on equilibrium mixture properties are investigated. In the next step, the effect of ethyl acetate (EA) as an additive on the equilibrium properties of the mixture is studied. The equilibrium properties of the mixtures, including CO2 solubility in the heavy liquid phase, the viscosity of the heavy liquid phase, and the densities of light and heavy liquid phases, are reported. The results suggest that the viscosity of bitumen is considerably reduced by mixing it with liquid CO2 at ambient temperature. It was also shown that the bitumen viscosity could be further reduced by the addition of ethyl acetate as a co-solvent.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.369
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.041
GPT teacher head0.293
Teacher spread0.251 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

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