Droplet Size Distribution in Twin Fluid Nozzle for Modern FCC Riser
Bibliographic record
Abstract
This study examines the spray characteristics of a specially designed nozzle tailored for riser applications.Through rigorous experimental analysis, the research aims to pinpoint the optimal operational parameters for the nozzle's design.Extensive experimental evaluations are conducted to gauge the atomizing performance of a twin-fluid injector and its potential integration into contemporary FCC feed systems.The innovative twin-fluid injector incorporates an impactor bolt strategically positioned at varying distances ahead of the liquid jet to enhance mixing dynamics and atomization performance.Using water and compressed air as working fluids, droplet sizes, and velocities are precisely measured by employing a phase Doppler particle analyzer.Results reveal a reduction in droplet size, as evidenced by a decrease in the SMD, attributed to the impactor bolt positioned 5 mm away from the center of the air injection orifice.Furthermore, the displacement of the spray axis, opposite the positioning of the impactor bolt, significantly influences droplet mean velocity.Droplet size diminishes with increasing mixing length, particularly in the core region, signifying improved atomization.Despite variations in slit size, both configurations exhibit a similar trend of decreasing droplet size with increasing mixing length, particularly evident in the core region, suggesting enhanced atomization.Thus, the effect of slit size on outcomes appears to be less significant compared to the impact of mixing length.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".