Prediction of global VLE for mixtures with improved renormalization group theory
Bibliographic record
Abstract
Abstract The recently proposed renormalization group (RG) theory is reformulated within the context of density functional theory and applied to predicting global vapor–liquid equilibria (VLE) of Lennard–Jones chain pure fluids and fluid mixtures. An accurate equation based on the solution of the first‐order mean‐sphere approximation (FMSA) is adopted outside the critical region. Inside the critical region, the direct correlation function of FMSA is incorporated into the new RG transformation to describe the long‐range fluctuation, which is conformal to general inhomogeneous studies. The new RG theory is applied to correcting real mixture phase envelopes, as well as corresponding phase diagrams of pure compounds for the critical region. The calculated results are in substantial agreement with those from experiment and molecular simulation both inside and outside the critical region. The new method is highly predictive because no adjustable parameters and no mixing rule are needed for both model and real fluid mixtures. © 2005 American Institute of Chemical Engineers AIChE J, 2006
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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 | 0.000 | 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) | 0.000 | 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".