Modelling carbon dioxide solubility in ionic liquids
Bibliographic record
Abstract
Abstract Solubility information for CO2 in different ionic liquids, ILs, in part can potentially be used to select a specific IL for the separation of CO2 from hydrocarbon fluids. Unfortunately, not all CO2–IL systems have been experimentally described at similar temperatures and pressures; therefore, a direct comparison of performance by process simulation is not always possible. In the extreme cases, the design of a CO2 separation process may require predicting the CO2–IL equilibria for which there are no available solubility data. To address the need for this information, a semi‐empirical correlation was developed to estimate the dissolution of CO2 in CO2–IL solvent systems. The theoretical COSMO–RS calculation method was used to calculate the chemical potential of CO2 in a wide variety of ILs and the Soave–Redlich–Kwong equation was used to calculate the fugacity coefficient of the CO2 vapour phase. The model was correlated with available literature data, yielding an average error of AAR = 23% and small bias. © 2012 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".