Regular solution model for asphaltene precipitation from bitumens and solvents
Bibliographic record
Abstract
Abstract A regular solution theory liquid‐liquid equilibrium model was developed to predict asphaltene precipitation from Western Canadian bitumens. The input parameters for the model are the mole fraction, molar volume, and solubility parameters for each component. Bitumens were divided into four main pseudo‐components corresponding to SARA fractions: saturates, aromatics, resins, and asphaltenes. Asphaltenes were divided into fractions of different associated molar mass based on a Schultz‐Zimm molar mass distribution. Asphaltene self‐association was accounted for through the average molar mass of the distribution. The molar volumes and solubility parameters of the pseudo‐components were calculated using solubility, density, and molar mass measurements. The model successfully predicted the effect of solvent type and associated molar mass on asphaltene precipitation for model oil and n‐alkane systems. The model also predicted the onset and amount of asphaltene precipitation from bitumens.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".