Applying the PR-EoS to Asphaltene Precipitation from <i>n</i>-Alkane Diluted Heavy Oils and Bitumens
Bibliographic record
Abstract
The Peng−Robinson equation of state (EoS) was adapted using group contribution methods to model asphaltene precipitation from solutions of toluene and an n -alkane and from n -alkane diluted bitumens. A liquid−liquid equilibrium was assumed between a primary liquid phase and a second dense asphaltene phase. Bitumen was characterized in terms of solubility fractions: saturates, aromatics, resins, and asphaltenes. Critical properties of the saturates, aromatics, and resins were determined that fit their measured densities and compared well with existing critical property correlations. The saturate and aromatic critical properties were also tuned to fit asphaltene precipitation data from solutions of the saturate and toluene or the aromatic and heptane. Asphaltenes were divided into fractions of different molar masses using a gamma distribution function. EoS parameters for asphaltenes were determined that fit the measured densities, fit precipitation data for mixtures of asphaltenes, toluene, and heptane, and compared well with existing critical property correlations. The model successfully fitted and predicted the onset and amount of precipitation over a broad range of compositions, temperatures from 0 to 100 °C, and pressures up to 7 MPa. The model results were within the error of the measurements except for high dilutions with n -pentane.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".