Measurement and Modeling of Asphaltene Precipitation from Crude Oil Blends
Bibliographic record
Abstract
A previously developed regular solution model was adapted to predict the onset and amount of asphaltene precipitation from crude oil blends diluted with pure n -alkanes or a mixture of toluene and n -heptane. Tests were conducted on nine different crude oils, a gas oil, and their blends. Oils and blends were characterized in terms of SARA (saturates, aromatics, resins, and asphaltenes) fractions. The mass fraction of each SARA fraction in the blends was confirmed as a weight average of the respective fraction in constituent oils. Asphaltenes were subdivided into fractions based on the gamma function to account for the distribution of aggregates resulting from self-association. To model the asphaltene onset and yield, liquid−liquid equilibrium was assumed between a heavy (asphaltenic) and a light (nonasphaltenic) phase. The distribution of asphaltenes in unblended crude oils was determined by fitting its asphaltene yield data when diluted with n -heptane. The fitting parameter in the model was the average aggregation number of asphaltenes in the source oils. Two approaches were tested to calculate the distribution of asphaltenes in crude oil blends. In the first approach, asphaltenes were assumed to interact, and the final molar mass distribution was determined from gamma function using the average aggregation number of constituent oils. In the second approach, no interaction was assumed, and the final distribution was calculated as a sum of the individual distributions. It was found that the best predictions of the onset and yield data were obtained by using the second approach. The mass fraction of n -heptane required to initiate precipitation was predicted with an average absolute deviation of 0.53% or less in all cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".