Screening ionic liquids as candidates for separation of acid gases: Solubility of hydrogen sulfide, methane, and ethane
Bibliographic record
Abstract
The solubility of the major constituents of natural gas in ionic liquids (ILs) can be used to identify their potential for acid gas removal from a producing gas stream. We have developed models for the solubility of H2S, CH4, and C2H6 in ILs at typical conditions encountered in natural gas treatment. In this work, a conductor‐like screening model for realistic solvation was used to predict the activity coefficients for solutes in ILs and a cubic EOS was used for vapor‐phase corrections from ideality. Empirical correlations were developed to extrapolate solubilities where experimental data are not available at desired conditions; targeted in this study at 298.15 K and 2000 kPa. Over 400 possible ILs were ranked based on the higher selectivity of absorption of CO2 and H2S over CH4 and C2H6. The best 15% (58) of promising ILs for sour gas treatment predominantly contain the anions BF4, NO3, and CH3SO4 and the cations N4111, pmg, and tmg. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2993–3005, 2013
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".