A multi-scale multi-stage model for spray freezing of binary solutions
Bibliographic record
Abstract
Spray freezing has found extensive applications in drying processes, drug delivery, and mine ventilation. The technique involves atomizing a liquefied material into a cold medium, where the resulting droplets solidify. This work presents a multi-scale model to study the spray freezing of binary mixtures. Specifically, the freezing model of a binary solution is coupled with a spray-droplet dynamics model. The spray freezing of an aqueous sucrose solution is analyzed using this framework, and parametric studies are conducted to examine the effects of droplet size, reactor length, ambient temperature, and liquid flowrate on the system performance. The results show that increasing the reactor length from 1.25 m to 4.25 m leads to a 79% increase in the final concentration. Lowering the ambient temperature from −15 °C to −25° yields a 2.63-times bigger solid-to-liquid fraction. Additionally, increasing the flowrate from 0.0125 kg/s to 0.0625 kg/s results in a 323% increase in heat rates.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".