Enhancement factor and kinetics of CO2 capture by MEA-methanol hybrid solvents
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Bibliographic record
Abstract
Although many studies have been done towards the solubility, enhancement factor and kinetic of the reaction between CO 2 and various alkanolamines, the reaction media are limited to aqueous solutions. This research focuses on study above characteristics of monoethanolamine (MEA)-methanol hybrid solvent. The objective of this research is to understand how the methanol mixed into MEA solution can enhance the CO 2 absorption. Three solvents used in this study are MEA aqueous solution, MEA in water-methanol 1:1 by volume, and MEA in methanol. The solubility of studied solvents was compared by Henry’s constant of pure solvent. The Kirkwood-Buff formalism was used to derive the Henry’s constant of CO 2 in water-methanol mixture. The bench-scale of CO 2 absorption in DX structuring column was carried out to evaluate the enhancement factor based on the film theory model. Based on zwitterions mechanism, reaction rate constant of the deprotonation step in MEA aqueous solution is the highest followed by MEA in methanol-water and MEA in methanol respectively, while the reaction rate constant of the formation step is in the reversed order. First order of reaction with respect to carbon dioxide is found for every representative solvent. The order of reaction respect to MEA ranges from 1.19 to 1.67 depending on the ratio of methanol in the solution. The reaction order is increased in the order of water, methanol-water and methanol. The result showed that higher performance of CO 2 absorption by MEA-methanol hybrid solvent is not because of improvement of kinetic reaction. On the contrary, methanol mixed in to MEA solution reduces the kinetic rate in the deprotronation step and cause the reduction of enhancement factor. It is because of the methanol mixed into MEA hybrid solvent enhances the diffusivity and solubility characteristics of the solvent that increase the CO 2 absorption rate.
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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 | 0.000 | 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) | 0.000 | 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 it