Amine Structure-Foam Behavior Relationship and Its Predictive Foam Model Used for Amine Selection for Design of Amine-based Carbon Dioxide (CO2) Capture Process
Bibliographic record
Abstract
Background: The use of an amine solution to capture CO 2 from flue gases is one of the methods applied commercially to clean up the exhaust gas stream of a power plant. One of the issues in this process is foaming which should be known in order to select a suitable amine for design. Objectives: In this work, all possible types of amines used for CO 2 capture, namely, alkanolamines, sterically hindered alkanolamines, multi-alkylamines and cyclic amines, were investigated to elucidate their chemical structure–foaming relationships. Methods: Foam volume produced by each type of 2M amine solution with its equilibrium CO 2 loading was measured at 40°C using 94 mL/min of N 2 flow. Results: Amines with a higher number or a longer chain of the alkyl group exhibited higher foam volume because of alkyl group’s ability to decrease the surface tension while increasing the viscosity of the solution. An increase in the number of hydroxyl or amino groups in the amine led to the reduction of foam formation due to the increase in surface tension and a decrease in viscosity of the solution. The predictive foam models for non-cyclic and cyclic-amines developed based on the structural variations, surface tension and viscosity of 29 amines predicted the foam volume very well with average absolute deviations (AAD) of 12.7 and 0.001%, respectively. The model accurately predicted the foam volume of BDEA, which was not used in model development with 13.3 %AD. Conclusion: This foam model is, therefore, indispensable in selecting a suitable amine for an amine-based CO 2 capture plant design and operation.
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 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 | 0.002 |
| 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 | 0.000 |
| 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 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".