A modified simplified <scp>SAFT EOS</scp> for <scp>VLE</scp> study of associating fluids
Bibliographic record
Abstract
Abstract In this study, the simplified version of statistical associating fluid theory (SAFT) equation of state (EOS) developed by Fu and Sandler is modified by replacing the dispersion term of this EOS with the Haghtalab–Mazloumi equation. This new SAFT‐based EOS has three adjustable parameters for non‐associating compounds and five adjustable parameters for associating compounds. The adjustable parameters of the new EOS are obtained by simultaneously fitting vapour pressures and liquid densities of pure substances. The new EOS shows better results in correlating vapour pressure and saturated liquid densities than SSAFT EOS for a selection of non‐associating and associating substances. Then, by using proper mixing rules, the new EOS is extended for mixtures. Both self‐associating and cross associating binary mixtures are used to test the capability of the new EOS in vapour–liquid equilibrium (VLE) calculations, and the results demonstrate good accuracy of the new EOS.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".