Prediction of Sanchez‐Lacombe equation of state characteristic parameters for plus fractions
Bibliographic record
Abstract
Sanchez‐Lacombe is one of the main polymeric equations of state that is used to predict thermodynamic properties of polymer solutions. Due to the similarity of the physical behaviour of polymer solutions and black oil, Sanchez‐Lacombe is also recommended to predict the liquid density of hydrocarbon fluids. In conventional thermodynamic equations of state, each component of the fluid is specified with some characteristic parameters such as the acentric factor, critical temperature, and critical pressure. Similarly, in the Sanchez‐Lacombe equation of state each component has some characteristic parameters which are obtained for pure components. Since there are no correlations for plus fractions (unknown fractions), the aim of this study is to present empirical correlations to predict the characteristic parameters of plus fractions based on experimental data. These empirical correlations are then used so that the Sanchez‐Lacombe equation of state can be applied to determine the liquid phase density of hydrocarbon fluids.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".