Understanding cubic equations of state: A search for the hidden clues of their success
Bibliographic record
Abstract
This work investigates the hidden details that are responsible for the practical success of cubic equations of state (EOS) in phase equilibrium calculations. A detailed consideration of the van der Waals method for evaluating the pure compound EOS parameters sheds new light on the reasons why the elimination of the actual critical volume as parameter was also adopted in the Redlich–Kwong and the Peng–Robinson (PR) frameworks. It is shown that an interesting relationship for the critical compressibility factor arising from the Martin–Hou method opens a new door for future exploration of different frameworks. A consideration of the key steps of Soave's reasoning for determining the temperature dependence of the attractive parameter explains the larger success of the Stryjek–Vera modification of PR EOS over the PR EOSs. A reference to the extension of cubic EOS to calculate liquid densities and enthalpies and a ready to use algorithm for the evaluation of the roots of a cubic equation are included for instructional purposes. © 2015 American Institute of Chemical Engineers AIChE J , 61: 2824–2831, 2015
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 | 0.001 | 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) | 0.000 | 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".