Characterization of the Contact between Liquid Spray Droplets and Particles in a Fluidized Bed
Bibliographic record
Abstract
The injection of a gas−liquid spray jet into a fluidized bed of particles is used in many applications such as fluid catalytic cracking or fluid coking. In such applications uniform contact of the liquid droplets and entrained particles is essential for high yields. The objective of this study is to measure the quality of the solid−liquid mixing when a gas−liquid jet is injected into a fluidized bed of coke particles. A quick method has been developed to determine the local quality of solid−liquid mixing on a short time scale. The measuring technique uses temperature to characterize the solid−liquid mixing. Cold ethanol is injected into the fluidized bed via a two-phase spray nozzle and is mixed with the heated fluidized coke particles. An assembly of fast response thermocouples, located downstream of the gas−liquid spray jet, provides instantaneous temperature readings over the liquid spray jet cross section at different axial positions along the length of the jet. In the case of perfect mixing, the temperature should be the same at each radial position. From the variations of the time-averaged temperature, contour plots of the liquid/solid distribution within the cross-sectional area of the jet are created. This technique is used to compare solid−liquid mixing for two cases: when the spray is introduced as a free jet and when a draft tube is placed downstream of the gas−liquid spray. The use of a draft tube is found to improve liquid/solid mixing. The measurement technique has proven to be a reliable method to determine the liquid/solid distribution in the cross section of the jet. It shows that very good and rapid contact between sprayed droplets and particles can be achieved by using a draft tube mixer.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".