Estimation of Isomeric Distributions in Petroleum Fractions
Bibliographic record
Abstract
This paper proposes a new approach to quantify the compositional distribution of different hydrocarbon isomers in an “isomeric lump” of a crude oil, determined using gas chromatography−mass spectrometry (GC-MS) methods. The concentration distribution of isomers can be determined with good accuracy by minimizing the Gibbs free energy of the mixture containing a set of isomers, subject to the stoichiometric constraint and the measured average boiling point of that isomeric lump. The simulated compositions of the hexane and heptane isomers were compared with the reported analytical results for 18 crude oils. The correspondence between predicted and measured distributions was found to be satisfactory. The experimental distributions of hexane and heptane isomers in those crudes are far from the thermodynamic equilibria, but the introduction of additional experimental information, in the form of the average boiling point of the lump, made it possible to model its isomeric distributions. This finding is important for the derivation of molecular representation for distillates in advanced kinetics modeling of refinery conversion processes.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".