Effect of Contaminants on the Gas Holdup and Mixing in Internal Airlift Reactors Equipped with Microbubble Generator
Bibliographic record
Abstract
The impact of contaminants on the gas holdup and mixing characteristics encountered in internal airlift reactors was investigated using a 200 L pilot scale unit equipped with a two-phase transonic sparger capable of generating microbubbles. Small dosages of a cationic surfactant (0–50 ppm of sodium dodecyl sulfonate (SDS)) were used to simulate the coalescence-retarding effect encountered in most industrial streams and resulted in the formation of bubbles that varied in size between 280 and 1,900 μm. Gas holdups as high as 0.14 were achieved in the riser under homogeneous flow regime when slowly coalescent systems were aerated at the relatively low superficial velocity of 0.02 ms−1, whereas liquid circulation velocities as high as 1.3 ms−1were achieved in conjunction with rapidly coalescent systems at the same superficial velocity. This excellent hydrodynamic performance represents a 5-fold improvement in the riser gas holdup and up to 8-fold enhancement in the liquid circulation velocity and is expected to yield good mixing and mass transfer performance at low energy dissipation rates.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".