Investigation of the Gas-Liquid Flow in an Agitated Vessel Equipped with an ASI Impeller by Using Tomography Method
Bibliographic record
Abstract
In this study, the hydrodynamics of the gas dispersion in an aerated vessel equipped with a newly designed impeller (ASI impeller), a combination of the pitched blade turbine and the Scaba impeller was investigated. The performance of the ASI impeller was compared with the performances of the pitched blade turbine and the Rushton impeller. The non-invasive Electrical Resistance Tomography (ERT) technique was used to assess the gas dispersion inside the mixing vessel. The effects of the volumetric gas flow, and impeller type and speed were investigated on power drawn, gas holdup, and mixing time. An analysis of the experimental data indicated that the ASI impeller exhibited a minimal effect of the gassing on the power drawn compared to the pitch blade and the Rushton turbine. Measured data on gassed power consumption indicated that the ASI impeller was about 45% more efficient than the Rushton turbine and 20% more than the downward PBT under the general operating conditions in bioreactors for gas flow rate about 1 vvm. Also, this impeller showed a higher amount of gas hold up and lower mixing time compared to the two other studied impellers. A dimensionless correlation for the relative power consumption as a function of flow number and Froud number was developed for the aerated agitated tank. Also, a dimensionless correlation was introduced to compute the overall gas hold up as a function of specific power consumption and superficial gas velocity for the gas-liquid agitated system. The overall conclusion from this study demonstrated that the axial-radial ASI impeller is a good energy-efficient impeller for the aeration system and bioreactors based on the results from the investigation of the data in terms of the power consumption, gas holdup, and mixing time.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".