Systematic Misprediction of <i>n</i>-Alkane + Aromatic and Naphthenic Hydrocarbon Phase Behavior Using Common Equations of State
Bibliographic record
Abstract
Phase equilibrium calculations based on 150 n -alkane + aromatic and n -alkane + naphthenic hydrocarbon binary mixtures were performed. These calculations were compared with experimental measurements whenever possible, and additional measurements were made as part of this work. The widely used Peng–Robinson (PR) and Soave–Redlich–Kwong (SRK) equations of state are shown to predict nonphysical liquid–liquid phase behavior for long-chain n -alkane + aromatic and long-chain n -alkane + naphthenic hydrocarbon binary mixtures with standard pure-component parameters ( T c, P c, ω). Incorrect phase behavior prediction is shown to be insensitive to the selection of correlations for estimating pure-component properties for n -alkanes that are not available from experimental data. For cubic equations of state, correct phase behaviors are obtained only when negative values of the binary interaction parameters ( k ij ) are used. For PC-SAFT, a noncubic equation of state (with standard parameter values defining molecules and with binary interaction parameters set to zero), phase behaviors that are consistent with observed phase behaviors are obtained. However, below the melting temperature of at least one of the components, liquid–liquid phase behavior is predicted for some binary mixtures. The quality of liquid/vapor phase composition and dew and bubble pressure predictions from the cubic and PC-SAFT models was not evaluated. Measurement and phase behavior modeling outcomes are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".