Comprehensive investigation of gas hold-up in a double coaxial mixer with shear-thinning fluids exhibiting yield stress: Experimental, numerical, and artificial neural network approaches
Bibliographic record
Abstract
This study addresses the challenge of uneven gas dispersion in yield-stress, non-Newtonian fluids, commonly encountered in industries such as biopharmaceuticals, cosmetics, and food processing. While previous research demonstrated the advantages of dual coaxial mixers for pseudoplastic fluids, limited attention has been given to aerating yield-pseudoplastic fluids with higher aspect ratios. This study bridges that gap by investigating both local and global gas hold-up under various conditions, utilizing electrical resistance tomography and computational fluid dynamics. Key findings showed that increasing the anchor speed from stationary to 30 rpm significantly enhanced aeration efficiency (gas hold-up per specific power consumption), with improvements of 78 % in UP-CO mode and 25 % in UP-COUNTER mode at N c = 350 rpm and Q g = 20 L/min. These results underscore enhanced gas dispersion under specific operating conditions, driving overall process intensification. To ensure accurate prediction of gas hold-up, both dimensional and dimensionless empirical correlations, along with an artificial neural networks (ANNs) model, were developed. The ANNs model exhibited superior accuracy, achieving R² values of 0.99 for both rotation modes, outperforming empirical models, which achieved R² values of 0.90 and 0.89 for UP-CO and UP-COUNTER modes, respectively.
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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 | 0.000 |
| 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 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".