Comparative study of eight cubic equations of state for predicting thermodynamic properties of alkanes
Bibliographic record
Abstract
Abstract Precise descriptions of the thermodynamic properties of pure fluids require accurate definition of vapour–pressure and phase volumes as well as residual volumes, enthalpies and entropies. While carefully fitted multi‐parameter equations of state (EOS), such as Benedict–Webb–Rubin–Starling fulfil these requirements, cubic EOSs usually do not. On the other hand, cubic EOSs are widely used in the oil industry, due to their simplicity and reliability in most vapour–liquid equilibrium calculations. For thermal oil recovery processes and the natural gas industry, the choice of EOS becomes important for predicting thermodynamic properties, such as isobaric and isochoric heat capacities, sound velocity and the Joule–Thomson coefficient. In this study, eight cubic EOSs which most of them are used in commercial reservoir simulators are selected for evaluation of their capability in the prediction of second‐order derivative thermodynamic properties at different temperatures and pressures, using pure components frequently found in petroleum and natural gas mixtures. It is shown that none of the cubic EOSs could accurately predict all of the stated parameters, especially below the critical point. All EOSs failed to show the extrema in the derivative properties. However, among these equations the Yu–Lu and Schmidt–Wenzel EOSs were found to have more reliable predictions in most of the cases. © 2011 Canadian Society for Chemical Engineering
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".