Vapour-Liquid Equilibrium in Binary Aqueous Mixtures using a Modified Regular Solution Model
Bibliographic record
Abstract
Phase equilibrium in binary mixtures of interest in wine and must distillation processes have been modelled using the Peng-Robinson equation of state. The mixing rules of Kwak and Mansoori and of Wong and Sandler were used. A new simple modification of the Regular Solution model for binary mixtures is also presented. The cases studied considered nine water+congener mixtures. The congeners included in the study are those regarded as legal compounds by the Chilean legislation for the production of a spirit made from grapes, called Pisco. The work allows concluding on the advantages, disadvantages and expected accuracy of the models used. L'équilibre des phases dans des mélanges binaires intervenant dans les procédés de distillation de vin et de mout a été modélisé à l'aide de l'équation d'état de Peng-Robinson. On a utilisé les règles de mélange de Kwak et Mansoori et de Wong et Sandler. Une nouvelle modification simple du modèle de solutions régulières pour les mélanges binaires est également présentée. Les cas étudiés considèrent neuf mélanges eau+congénère. Les congénères incluent dans l'étude sont ceux considérés comme des composants légaux dans la législation chilienne pour la production d'un vin spiritueux obtenu à partir de raisins, appelé Pisco. Ce travail permet de tirer des conclusions sur les avantages, les inconvénients et la précision possible des modèles employés.
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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 | 0.001 |
| 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".