Optimization of novel nonaqueous hexanol‐based monoethanolamine/methyl diethanolamine solvent for<scp>CO<sub>2</sub></scp>absorption
Bibliographic record
Abstract
Chemical absorption of CO2 into aqueous amine-based solvents is known as the most mature technology featuring high separation efficiencies and applicability of retrofitting. The high regeneration energy requirement of the process is a major drawback, and improvements in solvent design are required. This study investigated the CO2 absorption and desorption performance of the nonaqueous monoethanolamine (MEA)/methyl diethanolamine (MDEA) blend experimentally using a stirred cell reactor. CO2 loading, cyclic capacity loss, and initial absorption rate were measured for different solvent formulations and compared to single amines (MEA or MDEA). A mixture-process design and response surface methodology were employed to model and optimize the solvent formulation at different temperatures and pressures. The single-response optimization yielded 0.83 mol CO 2 / mol amine (at 0% MEA and 303 K/0.5 barg) and 3.17e-5 kmol/m2·s (at 40% MEA and 310.67 K/0.5 barg) as optimal absorption capacity and rate of absorption, respectively. The multiresponse optimization was conducted using the composite desirability function yielding 0.653 mol CO 2 / mol amine and 2.987e-5 kmol/m2·s (D = 0.903). The multiresponse optimization was also extended to include the impact of different initial settings and importance ratios between the absorption capacity and rate of absorption, in which the latter needed at least 1.6 higher importance level to influence the multiresponse optimization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".