Characterization of Minimum Impeller Speed for Suspension of Solids in Liquid at High Solid Concentration, Using Gamma-Ray Densitometry
Bibliographic record
Abstract
The successful design and operation of Liquid-Solid (LS) and Gas-Liquid-Solid (GLS) stirred tank reactors requires an accurate determination of the level of solid suspension needed for the process at hand. A poor design of the stirred tank to achieve optimum conditions and maintain the system under these conditions during operation may cause significant drawbacks concerning product quality (selectivity and yield) and cost. In this paper, the limitations of applying conventional measurement techniques for the accurate characterization of critical impeller speed for just off-bottom suspension ( N JS ) at high solid concentrations are described. Subsequently, the Gamma-Ray Densitometry technique for characterizing N JS is introduced, which can overcome the limitations of previous experimental techniques. The theoretical concept of this method is explained, and experimental validation is presented to confirm the accuracy of the Gamma-Ray Densitometry technique. The effects of clearance, scale, and solid loading on N JS for several impellers are discussed. Experimental N JS values are compared with correlations proposed in the literatures, and modifications are made to improve the prediction. Finally, by utilizing the similarity to the incipient movement of solid particles in other systems, a theoretical model for N JS prediction is presented.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".