Exploiting the prediction of mass transfer performance in aerated coaxial mixers containing biopolymer solutions using empirical correlations and neural networks
Bibliographic record
Abstract
Abstract The volumetric mass transfer coefficient is commonly used to assess the mixing effectiveness of gas–liquid bioreactor systems. Analyzing mass transfer performance in non‐Newtonian fluids inside coaxial mixers can be challenging due to the complex interaction between process variables, which requires developing robust characterization and estimation approaches. This study aims to investigate the gas dispersion in shear‐thinning biopolymers with yield stress using coaxial mixers in order to evaluate the effects of aeration and agitation on the volumetric mass transfer coefficient. A mixing configuration comprising a pitched blade turbine and an anchor was employed to disperse air into xanthan gum solutions, and the mass transfer performance was obtained at different impeller speeds and biopolymer concentrations by measuring the dissolved oxygen concentration. A dimensionless empirical correlation was proposed, and the results showed that the mass transfer was positively influenced by aeration intensity and agitation mechanism, quantified by the gas flow number and Reynolds number, respectively. Additionally, a strategy using stacking‐ensemble artificial neural networks was developed to accurately estimate the volumetric mass transfer coefficient, with a correlation coefficient of 0.998. The proposed mass transfer characterization approach overcame the complexities of analyzing aerated coaxial mixer systems and provided a reliable design model for bioreactor systems containing non‐Newtonian fluids.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".