Parametric Analysis of Mass-Transfer Performance in CO2 Absorber Using Aqueous MEA and MEA/MDEA
Bibliographic record
Abstract
To make the gas absorption process more economically viable, a rigorous design and operation strategy is an effective implementation. This work focuses on the development of a rigorous design and operation strategy for the absorber where the complicated mass-transfer of CO2with chemical reactions takes place. A series of absorption experiments were carried out in a bench-scale absorber packed with structured packings. Monoethanolamine (MEA) was used as absorption solvents. The absorption performance was analyzed and reported in terms of overall mass-transfer coefficient (KGae). The statistical factorial design analysis was performed to determine parametric influence and the synergy or interaction between process variables. The results show that CO2loading of solution is the most influential process variable on mass-transfer coefficient, followed by amine concentration, CO2partial pressure in feed gas, temperature, and liquid circulating rate, respectively. Liquid circulating rate interacts with amine concentration, while temperature appears to interact with all remaining variables. A second-order model of mass-transfer coefficient was successfully developed as a function of operating conditions. The knowledge obtained from this work can be used for developing a cost-effective strategy for design and operation of CO2absorber.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".