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
Why this work is in the frame
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Bibliographic record
Abstract
• The intensification of gas dispersion was enhanced by coaxial mixers. • Optimal anchor speed at 30 rpm enhanced aeration in yield-pseudoplastic fluids. • Increased apparent viscosity led to higher gas hold-up but uneven gas distribution. • The co-rotation mode exhibited a more even distribution of gas. • The ANN model predicted gas hold-up with a R 2 value of 0.99. 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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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 | 0.000 |
| 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 it