Modeling the Vapor-Liquid Equilibrium of Mixtures Involving Noble Gases, Alkanes, and Refrigerants and some Ionic Liquids Using Perturbed Hard-Sphere Equation of State
Bibliographic record
Abstract
The present study is a continuation of our previous work (S.M. Hosseini, J. Moghadasi, M.M. Papari, F. Fadaei Nobandegani, J. Mol. Liq. 160 (2011) 67-71) related to the examination of the ability of the perturbed hard- sphere equation of state (EOS) in predicting thermodynamic properties of pure fluid and mixtures.In our previous study, aperturbed hard-sphere equation of state was developed to predict pressure-volume-temperature-composition surfaces of pure and mixturesof ionic liquids (ILs). The present paper aims to extend the model to vapor-liquid equilibria of some binary mixtures consisting of ionic liquids, refrigerants, hydrocarbons, and monatomic fluids. The novelty of the present work is the application ofourperturbed hard-sphere equation of statetomodel the phase equilibria of various mixtures. The outcomes of the computation are compared with the experimental data. Our results demonstrate that this EOS can properly model phase equilibria of fluid mixtures with acceptable accuracies.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".