Predicting the Phase Behavior of Alcohols, Aromatic Alcohols, and Their Mixtures Using the Modified Group-Contribution Perturbed-Chain Statistical Associating Fluid Theory
Bibliographic record
Abstract
The modified group-contribution perturbed-chain statistical associating fluid theory (PC-SAFT) has been extended to model the phase equilibria of alcohols, branched alcohols, aromatic alcohols, and their mixtures. The parameterization has been implemented based on some physical arguments. The association energy of linear alcohols was fixed to the average experimental value, while this parameter for aromatic alcohols was directly estimated from the trimer hydrogen binding energy as evidenced by several experimental investigations. The dipolar moment of aromatic alcohols was reused from that reported experimentally. The importance of the association and the dipolar terms was investigated using the PC-SAFT equation of state by applying the model to represent liquid–liquid equilibrium (LLE) and vapor–liquid equilibrium (VLE) of several mixtures. The results obtained in this work suggest that including the dipolar term does not clearly improve the VLE prediction of linear alcohol-containing mixtures. It was possible to obtain good prediction results of VLE of alcohol-containing mixtures by using a nonzero binary interaction parameter to compensate for omitting the dipolar term (kij = 0.012, within a 5% deviation on bubble pressure for 95 mixtures with 2616 experimental data). However, the addition of a dipolar term with the 2B association scheme to the PC-SAFT has been proven necessary to correctly describe the LLE/VLE of aromatic alcohol-containing systems. Good LLE and VLE computation results were obtained for almost considered mixtures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.000 |
| 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".