Heat of Vaporization and Refractive Index Estimation for Hydrocarbons and Petroleum Fractions based on Simple Models
Bibliographic record
Abstract
The present research reports that simple two parameter and three parameter models integrating normal boiling point, specific gravity and molecular weight of hydrocarbons can effectively be used for estimation of different important properties namely heat of vaporization and refractive index. Multivariate regression analysis was employed for model development based on experimental data of pure hydrocarbons reported in literature. Subsequently, it has been demonstrated that this model can be utilized further for prediction of those properties for different petroleum fractions as well. The developed simple generalized two parameter regression-based models can predict heat of vaporization of pure hydrocarbons (C3 – C30) and petroleum fractions with wide boiling point range from 355.5 to 646.8 K with good accuracy (percentage error less than 10% for pure hydrocarbons and lower than 13% for petroleum fractions) in addition, refractive index of petroleum fractions are estimated with percentage error of less than 4.01%. Moreover, comparison results demonstrated that developed models are more accurate and simpler for practical applications in petroleum industry as compared to earlier published correlations for both pure hydrocarbons and petroleum fractions.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".