Experimental study of liquid vaporization in a fluidized bed
Bibliographic record
Abstract
Abstract Liquid injection into a gas–solid fluidized bed has been applied in various industries, such as coating and granulation processes in pharmaceutical and food industries, reactor cooling in polyolefin production, fluid catalytic cracking, and fluid coking in the petroleum industry. A new experimental method has been successfully developed to monitor the vaporization rate of a liquid injected into a fluidized bed. In addition, it can be used to determine the mass of liquid accumulated in the bed at a steady state. With this new method, measurements have identified three phenomena that may increase the amount of liquid accumulated at steady state in a fluidized bed. (1) Gas mixing affects vaporization when the bed temperature is lower than the liquid boiling point. In liquid‐rich regions of the bed, local vapour may build up, limiting the vaporization rate. Consequently, suitable emulsion to bubble gas transfer reduces the amount of accumulated liquid. (2) Solids mixing: hot particles from the rest of the bed must mix with the wetted particles to provide enough heat for vaporization. Good solids mixing reduces the amount of accumulated liquid. (3) Wet agglomerates formation: liquid trapped within wet agglomerates takes much longer to vaporize. The amount of accumulated liquid can be reduced by injecting the liquid in a well‐agitated bed region, operating at a higher bed temperature, or increasing the flowrate of atomization gas.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".