Mass Transfer Performance of CO2 Capture by Aqueous Hybrid MEA-Methanol in Packed Absorber
Bibliographic record
Abstract
Climate change and the emissions of greenhouse gases (GHGs) have become an important global problem. Carbon dioxide (CO2) emission from fossil fuel combustion is the primary contributor to this problem. Currently, a number of CO2capture technologies are technically feasible. Among these, gas absorption into chemical solvents is the most promising technology due to its capacity to handle a large volume of flue gas and can be operated at low temperature and pressure. One of the keys to successful operation of CO2chemical absorption process is the use of effective solvents. With hybrid solvents, mixtures of chemical and physical absorbents, the absorption capability at low partial pressure is enhanced or at least maintained. In addition, their performances at higher pressure are developed. This research was focused on the possibility of using hybrid solvent for CO2absorption. The mixture between monoethanolamine (MEA) and methyl alcohol was presented as hybrid solvent. The effects of main operating variables, such as CO2partial pressure, concentration of amine, the ratio of mixing, CO2loading, solvent flow rate and CO2gas flow rate were investigated. The performance of solvent for each operating parameter was compared by mean of mass transfer flux and overall gas-phase mass transfer coefficient of system.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".