The Paradox of Asphaltene Precipitation with Normal Paraffins
Bibliographic record
Abstract
For bitumens and crude oils, the volume of n -paraffin at the flocculation point, which is the point of incipient asphaltene precipitation, increases as the n -paraffin carbon number increases, reaching a maximum at a carbon number of 9 or 10, and then decreases. Thus, asphaltenes often can begin precipitating with a smaller volume of n -hexadecane than with n -pentane, even though large volumes of n -hexadecane precipitate much less (and more aromatic) asphaltenes than large volumes of n -pentane. How can n -hexadecane be both a better and a poorer solvent than n -pentane for asphaltenes? This paradox of solvent quality can be resolved by combining the entropy of mixing of molecules of different sizes with the heat of mixing from solubility parameters, as expressed by the regular Flory−Huggins model. With sufficient characterization data, the approximations and methods of Yarranton et al. can quantitatively describe asphaltene precipitation from the flocculation point to large excesses of n -paraffins from pentane to hexadecane. To describe only the flocculation point data of bitumens and crude oils, the oil compatibility model of Wiehe can be used. Although the oil compatibility model was derived on the basis that the solubility parameter is constant for a given oil at the flocculation point, using “effective” solubility parameters, flocculation points can be predicted with little characterization data.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".