Asphaltene precipitation from heavy oil mixed with binary and ternary solvent blends
Bibliographic record
Abstract
Abstract Models are required to predict the onset and precipitation of asphaltenes from mixtures of heavy oil and solvents for a variety of heavy oil applications. The regular solution approach is well suited for this objective but has not yet been tested on solvent mixtures. To do so, the onset and amount of asphaltene precipitation were measured and modeled for mixtures of heavy oil with solvent blends made up from n -alkanes, cyclohexane, and toluene at temperatures of 21 and 180 °C and pressures of 0.1 and 10 MPa. Temperature dependent binary interaction parameters (BIP) between the cyclohexane/asphaltene and toluene/asphaltene pseudo-component pairs were proposed to match the data. All other BIP were set to zero. The model with BIP determined from asphaltene precipitation in heavy oil and binary solvents predicted asphaltene precipitation from heavy oil and ternary solvent blends, generally to within the experimental error.
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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 | 0.000 |
| 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".