Methodology for the Characterization and Modeling of Asphaltene Precipitation from Heavy Oils Diluted with <i>n</i>-Alkanes
Bibliographic record
Abstract
A regular solution model, previously used to model asphaltene precipitation from Western Canadian bitumens, was tested on four international heavy oil and bitumen samples. The input parameters for the model are the mole fraction, the molar volume, and the solubility parameter for each component. Heavy oils and bitumens were divided into four main pseudo-components, corresponding to the SARA fractions (saturates, aromatics, resins, and asphaltenes). Asphaltenes were divided into fractions of different molar mass, based on a gamma molar mass distribution. The molar volumes and solubility parameters of the pseudo-components were calculated using solubility, density, and molar mass measurements and previously developed correlations. Model predictions were compared with the measured onset and the amount of asphaltene precipitation for solutions of asphaltenes in toluene and n -heptane and for heavy oils diluted with n -alkanes, all under ambient conditions. The overall average absolute deviations (AAD) of the predicted fractional precipitation or yields were <0.031 for the asphaltene solutions and <0.008 for the diluted heavy oils. A methodology for characterizing heavy oils and modeling asphaltene precipitation from n -alkane-diluted heavy oils is proposed.
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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 | 0.000 |
| Open science | 0.001 | 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".