Estimation of the CO<sub>2</sub>Absorption Capacities in Aqueous 2-(2-Aminoethylamino)ethanol and Its Blends with MDEA and TEA in the Presence of SO<sub>2</sub>
Bibliographic record
Abstract
A study of carbon dioxide (CO 2 ) and sulfur dioxide (SO 2 )/CO 2 mixtures absorption has been carried out in aqueous 2-(2-aminoethylamino)ethanol (AEE) solution and its blends with N -methyldiethanolamine (MDEA) and triethanolamine (TEA) to estimate the influence of SO 2, MDEA, and TEA on the CO 2 absorption capacity of the AEE. The CO 2 absorption loading has been estimated in 15 wt % AEE alone and in the presence of either 5 and 10 wt % MDEA or 5 and 10 wt % TEA solutions with 100 vol % CO 2 and 5.03 and 15.02 vol % SO 2 /CO 2 mixtures at a starting temperature of 296 K and flow rates of 3.067, 3.229, and 3.605 L/min, respectively. The results revealed that the presence of SO 2 in the gas decreases the CO 2 absorption rate and loading in the AEE solution as a function of the concentration of SO 2 . The additions of 5 and 10 wt % of MDEA and TEA do not seem to influence the CO 2 absorption rate in the AEE solution. Moreover, the addition of MDEA increases slightly the CO 2 absorption capacity of AEE, while TEA decreases the absorption capacity of AEE in the absence and presence of SO 2 . These effects were enhanced with increases of MDEA and TEA. Altogether, the results indicated that the blend of 15 wt % AEE + 10 wt % MDEA represents an interesting solvent which could be used as absorbent for the removal of CO 2 from emission into the atmosphere by industries.
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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 | 0.000 |
| 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) | 0.001 | 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".