A Novel Method to Develop a New Scaling Equation for Modeling of Asphaltene Precipitation
Bibliographic record
Abstract
Abstract Most investigations showed that the prevalent thermodynamic models are incapable of predicting asphaltene precipitation without extensive data fitting. This is primarily due to lack of knowledge of the asphaltene properties, its complex nature and the large number of parameters affecting precipitation. Hece, several authors tried to generate a simple and universe mathematical model in order to predict the amount of asphaltene precipitation. In spite of these efforts, the authors only considered temperature and type of solvents as the effective parameters in generating their scaling equations. The major disadvantage of these models is their inability in predicting the amount of asphaltene precipitation for different crude oils. Therefore, this deficiency contradicts to the universality of these models. In this work by performing experimental tests on different crude oils and analyzing their properties such as GOR, Resin to Asphaltene ratio, mole percent of plus fractions and residual oil density, a new scaling equation was developed for the predicting of asphaltene precipitation of different crude oil samples. The developed correlation can be used to estimate the amount of precipitated asphaltene at different dilution ratios and the onset dilution ratio of precipitation. The new scaling correlation has been validated with another experimental precipitation data generated after developing the scaling correlation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".