Characterization of Heavy Oils and Bitumens 2. Improving the Prediction of Vapor Pressures for Heavy Hydrocarbons at Low Reduced Temperatures Using the Peng−Robinson Equation of State
Bibliographic record
Abstract
An enhanced form of the temperature-dependent attractive pressure term of the Peng−Robinson equation of state (PR EOS) was developed. The function was developed using experimental vapor pressures from the NIST Standard Reference Data Base #103 as well as vapor pressures estimated based on the Nji et al. (2008) vapor pressure prediction correlation. 6000 vapor pressure data points from 237 diverse hydrocarbons were used in this study to develop the new attractive term. The quality of predicted vapor pressures using the modified PR EOS for heavy hydrocarbons showed a significant improvement when compared against the standard equation of state. Between reduced temperatures of 0.3 and 0.8, the average and maximum absolute percentage deviations for the predicted vapor pressures for the modified PR EOS are 4.9% and 78.0%, respectively, as compared to 10.4% and 230% for the standard PR EOS. For hydrocarbons from methane to tetralin corresponding to C 1 −C 10, the average and maximum absolute percentage deviations in the vapor pressures using the standard Peng−Robinson are 5.7% and 70.7%, respectively. Using the modified PR EOS, the average and maximum percentage deviations in the calculated vapor pressures are 4.6% and 35.6%, respectively. This enhanced attractive pressure term combined with the critical property estimation method presented by Nji et al. (2008) provides a simple and self-consistent method for the prediction of thermodynamic properties of heavy hydrocarbons.
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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".