Modified α Function for the Peng−Robinson Equation of State To Improve the Vapor Pressure Prediction of Non-hydrocarbon and Hydrocarbon Compounds
Bibliographic record
Abstract
On the basis of the available vapor pressures for 59 non-hydrocarbon and hydrocarbon compounds, including heavy alkanes up to n -tritetracontane ( n -C 43 H 88 ), a modified α function for the Peng−Robinson equation of state (PR-EOS) has been developed to more accurately determine the vapor pressure for pure non-hydrocarbon and hydrocarbon compounds, especially heavy components. To balance the characterization of both light and heavy compounds, the Pitzer acentric factor is first redefined in terms of reduced vapor pressure at a reduced temperature of 0.6. In comparison to the evaluated α functions used for the PR-EOS, it is found that the newly developed α function with the redefined acentric factor provides a more accurate prediction of vapor pressures with a percentage average absolute deviation of 1.90% and a percentage maximum absolute deviation of 21.22% for the 59 chemical species. In addition, the newly developed α function results in the best prediction of the vaporization enthalpy data with an average absolute deviation of 3.92% in comparison to the other existing α functions evaluated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".