Vapor−Liquid Equilibrium of Systems Containing Alcohols Using the Statistical Associating Fluid Theory Equation of State
Bibliographic record
Abstract
The statistical associating fluid theory (SAFT) equation of state is employed for the correlation and prediction of vapor−liquid equilibrium (VLE) of binary mixtures of alcohols with water, carbon dioxide, butane, hexane, benzene, and other alcohols. In addition, ternary VLE for water/1,2-propanediol (propylene glycol)/1,2-ethanediol (ethylene glycol), carbon dioxide/methanol/ethanol, and water/1,3 propanediol/1,2,3-propanetriol (glycerol) mixtures is predicted. In the SAFT equation, three molecular parameters, the Lennard-Jones (L-J) potential well depth, the soft-sphere diameter of the segments, and the number of segments of the molecule, are needed. These parameters are obtained from the thermodynamic properties of pure substances. For self-associating pure substances, two additional parameters are needed, namely, the bonding volume and the association energy. The binary interaction parameters are fitted to experimental vapor−liquid equilibrium data for binary systems. These binary parameters are used to predict the phase equilibria for ternary mixtures without any additional adjustment. The results were found to be in good agreement with the experimental data.
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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) | 0.001 | 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 | 0.001 |
| 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".