Liquid–liquid equilibrium studies of <i>n</i> ‐butane–bitumen system with application to solvent‐aided in situ bitumen recovery
Bibliographic record
Abstract
Abstract Solvent‐assisted recovery methods have gained attention as an alternative to conventional thermal methods which have significant environmental drawbacks and high greenhouse gas emissions. Solvent‐assisted recovery methods can lead to various phase equilibria, such as vapour–liquid equilibrium (VLE) and liquid–liquid equilibrium (LLE), under specific thermodynamic conditions. A thorough understanding of this phenomenon is essential for effectively designing and optimizing these processes. Despite the availability of both VLE and LLE for mixtures of solvent‐bitumen systems, LLE data is scarcer, particularly at conditions close to the solvent's saturation pressure. This study reports on LLE measurements of n ‐butane–bitumen mixtures at temperatures ranging from 295 to 372 K. The pressures were set just above the solvent's saturation pressure at each temperature to maintain LLE conditions. We examined both the phase behaviour and thermophysical properties of the mixture. The thermophysical properties of the light phase, including density and viscosity, were recorded. The bubble point pressure, n ‐butane content in the light phase, heavy phase onset, and detailed characterization of both the light and heavy cuts were also conducted. The experimental thermophysical data were then modelled using the Peng–Robinson equation of state (PR‐EoS) and modified Pederson model. In conclusion, these findings enhance our understanding of the LLE behaviour of n ‐butane–bitumen systems under conditions near the solvent's saturation pressure and find plications in solvent‐aided in situ bitumen recovery processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".