Kinetics of CO2 Capture by Blended MEA-AMP
Bibliographic record
Abstract
Carbon dioxide (CO2) is the largest contributor among greenhouse gases (GHGs) in terms of emissions. Capturing CO2from industrial gas stream by aqueous alkanolamine solution is the most cost-effective technology available today. Monoethanolamine (MEA) has been commonly used in gas processing industry for decades. In recent years, a sterically hindered amine, 2-amino-2-methyl-1-propanol (AMP), has gained its popularity since it offers a higher absorption capacity and a lower energy consumption during regeneration compared to MEA. Blending MEA with AMP is predicted to combine all favorable characteristics of both solvents and overcome the unfavorable characteristics. To date, the feasibility of using this blended MEA-AMP has been investigated through fundamental studies, especially in the area of thermodynamics. This work focuses on another fundamental aspect, i.e. kinetics of aqueous MEA-AMP. The kinetic measurements were carried out in a wetted wall column under ranges of process conditions. The column made from a 100 mm-long stainless steel tubing was fitted inside a glass chamber where the temperature of absorption was precisely controlled. The reaction kinetics was interpreted in terms of overall rate constant. Results show that reaction kinetics of MEA-AMP vary with process parameters including mixing ratio of MEA and AMP and absorption temperature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".