Mass Transfer Performance of CO<sub>2</sub> Absorption into Aqueous Solutions of 4-Diethylamino-2-butanol, Monoethanolamine, and <i>N</i>-Methyldiethanolamine
Bibliographic record
Abstract
The mass transfer performance of the absorption of CO 2 in an aqueous solution of monoethanolamine was evaluated experimentally in a lab-scale absorber packed with high efficiency DX structured packing and compared with that of methyldiethanolamine (MDEA) as well as that of a newly developed tertiary amino alcohol, 4-diethylamino-2-butanol (DEAB). The absorption experiments were conducted at atmospheric pressure, using a feed gas mixture containing 14.9% CO 2 and 85.1% nitrogen in an absorption column containing DX structured packing. The absorption performance was presented in terms of the CO 2 removal efficiency, absorber height requirement, effective interfacial area for mass transfer, and overall mass-transfer coefficient ( K G a v ). In particular, the effects of parameters such as inert gas flow rate and liquid flow rate were compared for both DEAB and MDEA. The results show that the DEAB has a much higher removal efficiency for CO 2 along the height of the column than MDEA. Also, the K G a v of DEAB was much higher than that for MDEA. For all the solvents, the K G a v increased as the liquid flow rate was increased. An empirical correlation for the mass transfer coefficient for the CO 2 -DEAB system has been developed as a function of the process parameters. In terms of comparison, the results show that the DEAB system provided an excellent overall mass transfer coefficient, which is higher than that of the MDEA system but less than that of MEA.
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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".