Phase equilibrium in<i>n</i>-octane/water separation units: vapor pressures, vapor and liquid molar fractions
Bibliographic record
Abstract
Abstract The present study reports result from research into vapor–liquid–liquid phase equilibrium forn-octane highly diluted in water and water highly diluted inn-octane blends, using a dynamic method implemented in a constant volume CREC-VL-Cell. In the CREC-VL-Cell, a very high level of mixing is achieved, allowing for dispersions to be formed in the liquid phase and good mixing in the gas phase. This VL-Cell and its auxiliary equipment provide an increasing temperature ramp in the 30–110 °C range. It is found that the CREC-VL-Cell is of special value, for studying immiscible or partially miscible blends, such as is the case ofn-octane in water. With the data obtained, which includes vapor pressures and temperatures, data analyses involving mass and molar balances, allow establishing overall liquid and vapor molar fractions. The recorded vapor pressures together with the calculated liquid and vapor molar fractions offer valuable data for VL thermodynamic model discrimination. For instance, it can be shown that vapor pressures, vapor and liquid molar fractions, as calculated with the Aspen-Hysys Peng Robinson Equation of State (Hysys-Aspen PR-EoS) provide only a first approximation of the experimental data, with significant discrepancies in the prediction of ann-octane disengagement temperatures. Thus, the determination of combined measured vapor pressures and calculated overall liquid molar fractions in the CREC-VL-Cell, offers a valuable and accurate procedure for thermodynamic model validation and discrimination.
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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 | 0.000 |
| 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 | 0.000 |
| 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".