Toward accurate density and interfacial tension modeling for carbon dioxide/water mixtures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Phase behavior of carbon dioxide/water binary mixtures plays an important role in various CO 2 -based industry processes. This work aims to screen a thermodynamic model out of a number of promising candidate models to capture the vapor–liquid equilibria, liquid–liquid equilibria, and phase densities of CO 2 /H 2 O mixtures. A comprehensive analysis reveals that Peng–Robinson equation of state (PR EOS) (Peng and Robinson 1976), Twu α function (Twu et al. 1991), Huron–Vidal mixing rule (Huron and Vidal 1979), and Abudour et al. (2013) volume translation model (Abudour et al. 2013) is the best model among the ones examined; it yields average absolute percentage errors of 5.49% and 2.90% in reproducing the experimental phase composition data and density data collected in the literature. After achieving the reliable modeling of phase compositions and densities, a new IFT correlation based on the aforementioned PR EOS model is proposed through a nonlinear regression of the measured IFT data collected from the literature over 278.15–477.59 K and 1.00–1200.96 bar. Although the newly proposed IFT correlation only slightly improves the prediction accuracy yielded by the refitted Chen and Yang (2019)’s correlation (Chen and Yang 2019), the proposed correlation avoids the inconsistent predictions present in Chen and Yang (2019)’s correlation and yields smooth IFT predictions.
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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 it