Methodology for the Characterization and Modeling of Asphaltene Precipitation from Heavy Oils Diluted with <i>n</i>-Alkanes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it