Simulation study on atomization characteristics of swirl nozzle for dust removal of coke dry quenching
Bibliographic record
Abstract
Abstract To study the atomization characteristics of nozzles during the wet dust removal process of coke dry quenching (CDQ), the swirl nozzle, plain orifice nozzle, air‐blast atomizing nozzle, and effervescent atomizing nozzle commonly used in engineering were simulated. Droplet distribution and particle size produced by different types of nozzles were obtained by using Fluent software. The simulation was performed using the realizable k ‐ ε turbulence model, the discrete phase model (DPM), and the Taylor analogical breaking (TAB) model. While simulating the atomization characteristics of pressure swirl nozzle at different diameters and pressures, it was found that a nozzle with a diameter of 1.5 mm produced smaller droplet sizes and a more uniform distribution. The droplets near the nozzle had a small size, were affected by airflow and gravity, and collided with other droplets during migration, resulting in the increase of some droplet size. At the water supply pressure of 10 or 12 MPa, the size of almost atomized droplets was below 100 μm. Diameter of 1.5 mm pressure swirl atomizing nozzle at the water supply pressure of 12 MPa is the preferred working conditions of CDQ dust removal. When Ca(OH) 2 solution was used as the medium for atomization dust removal and SO 2 adsorption, the spray particle size range was 4.44 × 10 −5 ~ 2.94 × 10 −4 m, which was smaller and more uniform than when the water was used as the medium. This study could provide preliminary data reference for the collaborative removal of dust and gaseous pollutants in the wet dust removal process of CDQ.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".