Computational modelling and optimization of physicochemical absorption of <scp> CO <sub>2</sub> </scp> in rotating packed bed
Bibliographic record
Abstract
Abstract The current study developed a novel computational fluid dynamics (CFD) model that accounted for both physical and chemical absorption in the multiphase flow and captured the relative dominance of chemical absorption over physical by employing a tunable model parameter ‘enhancement factor’. The CFD model was validated against experimental data in a rotating packed bed, and then the validated model was used to investigate the effect of operational parameters such as rotational speed, monoethanolamine (MEA) concentration, inlet velocity, and MEA‐packing contact angle on the physiochemical absorption. The significance of each operational parameter was then evaluated by the ANOVA analysis, which inferred that the enhancement factor is sensitive to rotational speed, MEA concentration, inlet velocity, and contact angle. The p ‐value of MEA concentration and inlet velocity was less than 0.05, which implies that these two variables are the most significant variables for the chemical absorption of CO 2 . The response surface methodology (RSM) and the artificial neural network (ANN) were also employed to develop the predictive model for the enhancement factor. Among the employed techniques, ANN resulted in R 2 closer to 0.99 and could better predict the enhancement factor. The modelling approach and findings of the current study are useful in optimizing the operation of rotating packed‐bed reactor (RPB) for CO 2 absorption on the industrial scale.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".