Basic design methodology for a prilling tower
Bibliographic record
Abstract
Abstract A design methodology was developed for prilling towers based on simultaneous heat, mass, and momentum balances. Basic principles of the prilling process and related sub‐models were considered for production of relatively mono‐size prills. The method was employed in the design of a prilling tower for production of ammonium nitrate prills from a highly concentrated solution. A special showerhead spray, operating under laminar conditions in a Rayleigh jet break‐up regime, was designed to enhance production of mono‐size prills. Air was used at ambient conditions. The droplets leaving the showerhead fall through the air stream in a counter‐current fashion. Heat transfer from particles to air includes cooling in the fully liquid state, solidification, and cooling in the fully solid state. The Computational Fluid Dynamics (CFD) simulation revealed the formation of a quiescent zone around the nozzle discharge region, which had a significant effect on decreasing the secondary disintegration of the droplets, and hence narrowed down the size distribution towards the desired value. The size of the produced prills was larger than the predicted value due to the formation of ammonia bubbles inside the prills.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".