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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".