Application of Peng-Robinson Equation of State for Calculating Solid-Vapor and Solid-Liquid Equilibrium of CH4-CO2 System
Bibliographic record
Abstract
In this study, the performances of Peng-Robinson Equation of State combined with the classical mixing ruleswere evaluated to calculate solid-vapor equilibrium (SVE) and solid-liquid equilibrium (SLE) of CH4-CO2system. The evaluation was performed by comparing the calculated values with the literature data. In thecalculation of SVE, the new binary interaction parameter values (kij) of CH4-CO2 mixtures were proposed in thiswork based on the experimental data. The proposed kij obtained in this work might increase the accuracy ofPeng-Robinson Equation of State by reducing average absolute deviation in the temperatures between calculatedvalues and literature data from (2.18% to 0.26%), (0.88% to 0.70%) and (0.61% to 0.44%) at CO2 compositionof 1%, 1.91% and 2.93%, respectively. Significant improvement was found at CO2 composition of 1%. In thecalculation of SLE, new parameters were not proposed since the calculation using the existing (literature) kijgives good results with an average absolute deviation of 0.5%.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".