An improved Wilson equation for phase equilibrium <i>K</i> values estimation
Bibliographic record
Abstract
Abstract Phase equilibrium K values are either estimated with empirical correlations or rigorously calculated based on fugacity values determined from an equation of state. There have been several empirical analytical equations such as Raoult’s Law, the Hoffman Equations (Hoffman A, Crump J, Hocott C. Equilibrium constants for a gas condensate system. J Petrol Technol 1953;5:1–10) and their modifications and the well-known Wilson Equation (Wilson G. A modified Redlich–Kwong equation of state applicable to general physical data calculations. In: AIChE National Meeting Paper15C, May 4–7, Cleveland, OH; 1969). along with several modifications. This work presents a new modification of the Wilson Equation for estimating phase equilibrium K values, predominantly for light hydrocarbon mixtures. The modification is based on correlating a subset of a database of K values, established from convergence pressure data. Results show the method to accurately correlate and predict the K value data, within 10% on average. Moreover, the predicted K factors provide remarkable results for such a simple model when used in a variety of phase equilibrium calculations. The results also show that the new model compares favorably with existing empirical analytical methods. Such a model would provide excellent initial estimates for rigorous thermodynamic calculations.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".