Digital X-ray Imaging Technique to Study the Horizontal Injection of Gas-Liquid Jets into Fluidized Beds
Bibliographic record
Abstract
Gas-liquid jets injected into fluidized beds of particles/catalyst find applications in many industrial processes. The effective distribution and mixing of the feed droplets with the entrained bed particles is vital in improving the process efficiency. The present study utilizes a sophisticated digital X-ray imaging system to study the internal flow structure of jets injected into fluidized beds. The system is equipped with an X-ray image intensifier (XRII) and optical detectors, which convert the transmitted X-ray photons into digital images of up to 60 frames s-1. The imaging technique provides useful information such as the jet expansion angle and the penetration distance. These are functional quantities in optimizing the performance of feed nozzles, and in modeling the jet-fluidized bed interactions.In this work, the horizontal injection of gas, gas-liquid, and liquid jets into fluidized beds is investigated. The results indicate that the jet expansion (half-angle) is considerably reduced for a gas-liquid jet (5-7 degrees) when compared to that of a gas jet (10-15 degrees). The gas-liquid jet also appears to penetrate more than a gas jet with the same momentum. When a liquid feed is introduced into a fluidized bed of particles, the particles may agglomerate if they are wet-enough to form liquid bridges. Improper feed distribution may be a direct contributor to enhanced agglomeration. In this regard, radio-opaque tracers mixed with the feed liquid are injected to track the formation and the movement of agglomerates. The tracer experiments show that the agglomerates are generated at the end of the jet region, close to its maximum penetration distance. A brief discussion on the modifications required to achieve improved contrast for the acquired images, and the effect of some important X-ray parameters are also included in the present study.
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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 | 0.000 |
| 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".