Analysis of gas holdup and bubble behavior in a biopolymer solution inside a bioreactor using tomography and dynamic gas disengagement techniques
Bibliographic record
Abstract
Abstract BACKGROUND The interfacial mass transfer rate in an aerated system vessel in which the liquid phase is in contact with the gas phase is closely related to the interfacial area between two phases. The gas–liquid interfacial area strongly depends on the bubble size, bubble size distribution, and the gas holdup. For the first time, in this study the performance of the ASI impeller was assessed by determining the bubble classes, bubble size distribution, local gas holdup, and global gas holdup in an aerated mixing system containing a highly viscous and non‐Newtonian biopolymer solution. The effects of the volumetric gas flow, impeller speed, and fluid rheology on the gas dispersion attained by the ASI impeller were investigated. RESULTS Electrical resistance tomography (ERT) was coupled with the dynamic gas disengagement (DGD) technique to determine the bubble classes, to measure the gas holdup, and to calculate the Sauter mean bubble diameter. The performance of the ASI impeller was compared with those of the pitched blade turbine and the Rushton impeller in terms of the bubble size distribution and gas holdup. The experimental data revealed that three classes of bubbles were formed within the aerated system. An empirical correlation for the gas holdup as a function of the gas flow number and apparent viscosity of fluid was developed for the aerated mixing tank equipped with the ASI impeller. CONCLUSION The ASI impeller effectively enhanced the breakage of the bubbles and gas holdup at the higher gas flow rates in a yield‐pseudoplastic fluid. © 2017 Society of Chemical Industry
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".