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Record W4409593952 · doi:10.1016/j.fluid.2025.114442

Solubility of water in bitumen

2025· article· en· W4409593952 on OpenAlexfundno aff
B. Zuluaga, F. F. Schoeggl, Harvey W. Yarranton

Bibliographic record

VenueFluid Phase Equilibria · 2025
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaConocoPhillips CanadaAlberta InnovatesCanada Excellence Research Chairs, Government of CanadaSuncor Energy IncorporatedCanadian Natural Resources Limited
KeywordsChemistryAsphaltSolubilityEnvironmental chemistryOrganic chemistryChemical engineeringComposite material

Abstract

fetched live from OpenAlex

Liquid/liquid-vapor (L/LV) and aqueous-liquid/aqueous-liquid-vapor (AL/ALV) boundaries of pseudo-binary mixtures of bitumen and water were measured using the isothermal stepwise volume expansion method at conditions relevant to in situ heavy oil operations (temperatures from 180 to 280 °C and pressures from 1.5 to 5 MPa). The L/AL boundary was determined from the intersection of the L/LV and AL/ALV boundaries. An activity coefficient model of the pseudo-binary system was used to check the self-consistency of the L/LV measurements. The Advanced Peng Robinson equation of state, which has a distinct alpha function for water, was used to model the VLE and VLLE data. For this model, the oil was characterized into pseudo-components based on a SimDist assay and the specific gravity and asphaltene content of the oil. Temperature dependent binary interaction parameters between bitumen pseudo-components and water were tuned such that the model fit the measurements to within the experimental error. Isothermal pressure-composition phase diagrams were generated for the pseudo-binary mixtures at each temperature. Finally, a straightforward correlation for the solubility limit of water in bitumen as a function of temperature was developed using the data in this study and from the literature. The average deviation of the correlation was 0.5 wt % below 340 °C.

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 score0.489
Threshold uncertainty score0.659

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 head0.254
Teacher spread0.247 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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