Prediction of the <scp>ASTM</scp> and <scp>TBP</scp> distillation curves and specific gravity distribution curve for fuels and petroleum fluids
Bibliographic record
Abstract
Abstract A predictive approach called the six‐point method is developed to construct distillation curves of different petroleum fluids based on the information of six points of the distillation curve at volume percentages (5, 10, 30, 50, 70, and 90). Indeed, six diverse mathematical equations are developed, which establish relationships between the six mentioned distillation temperatures and physical properties ( and ) of petroleum fluids. Having predicted the six points, a third‐order polynomial curve can easily be fitted over these data. Accordingly, the six‐point method is well capable of predicting the distillation curve of both American Society for Testing and Materials (ASTM) D86 and true boiling point (TBP) data for any arbitrary petroleum fluid using only their physical properties. Two auxiliary relations are also presented to estimate the physical properties that may not be available for a petroleum fraction. These relations enable the model to receive pairs of () and () as input parameters, instead of (). A useful transfer function is also developed to convert the TBP curve predicted by the six‐point method to the specific gravity distribution curve. Furthermore, a new set of experimental data obtained from the distillation test is also presented in this work. The experiment has been performed on the petroleum cuts resulting from the crude oil atmospheric distillation unit and Linear Alkyl Benzene (LAB) production unit. The model performance was carefully evaluated against a wide range of literature data as well as new experimental data provided in this study, and the mean of average absolute deviations (AAD%) values was 1.98% for about 233 samples, including 2039 data points.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".