Application of the Modified Regular Solution Model to Crude Oils Characterized from a Distillation Assay
Bibliographic record
Abstract
The modified regular solution model was developed to predict onset and amount (yield) of asphaltene precipitation from mixtures of crude oil and solvents. The most recent version of the model includes the partitioning of all components between a solvent-rich phase and an asphaltene-rich phase, where the solvents are pure components with known properties and the crude oil is represented as pseudo-components defined on the basis of a saturate, aromatic, resin, and asphaltene (SARA) assay. The molecular weight, density, and solubility parameter of each pseudo-component are determined from correlations. This model is sensitive to uncertainties in the SARA assay composition and is not compatible with the standard phase behavior modeling methodology based on pseudo-components defined from a distillation assay. In this contribution, the model was extended to crude oil pseudo-components based on boiling cuts (TBP). The n -pentane-insoluble asphaltene fraction was characterized in the same way as the SARA-based model. The molecular weight and density of the TBP-based pseudo-components were predicted from well-established correlations. New correlations were proposed for the maltene pseudo-component solubility parameters as a function of the temperature and pressure. The TBP-based model was tested on two data sets: (1) onsets, yields, and phase compositions for a Western Canada bitumen mixed with n -alkanes from propane to n -heptane at temperatures and pressures up to 250 °C and 13.8 MPa, respectively, and (2) yields from eight oils from different geographical regions mixed with n -alkanes from n -pentane to n -octane at temperatures and pressures up to 100 °C and 6.8 MPa, respectively. The overall absolute deviation was 2 wt % for both data sets, similar to the deviations found for the SARA-based model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".