Can a Cubic Equation of State Model Bitumen–Solvent Phase Behavior?
Bibliographic record
Abstract
Cubic equations of state (CEoS), such as the advanced Peng–Robinson (APR) EoS, are convenient for use in commercial simulators and have successfully fit saturation pressures and asphaltene onset points for bitumen–solvent systems using simple quadratic mixing rules. However, this approach does not accurately predict asphaltene precipitation yields. In this study, the APR EoS with several sets of asymmetric mixing rules is evaluated against saturation pressure and asphaltene yield data for n -pentane diluted bitumen. The asymmetric van der Waals, Sandoval et al., and two forms of Huron–Vidal mixing rules with an NRTL (non-random liquid theory) activity coefficient model are considered. The use of asymmetric mixing rules significantly improves the match to asphaltene yield data; however, the yields are still underpredicted at high solvent contents, and the tuning parameters that give the best match for asphaltene yield data are not predictive or easily correlated for other solvents. The APR EoS with symmetric van der Waals mixing rules is also evaluated with compositionally dependent binary interaction parameters. The use of compositionally dependent solvent/asphaltene binary interaction parameters allows the model to fit asphaltene yield data over the entire composition range. A set of interaction parameters is recommended that fits both asphaltene yield and saturation pressure data. The merits of this methodology as a practical option for modeling heavy oil–solvent behavior are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".