Effect of Temperature and Successive Sprays on Liquid Distribution in Fluidized Beds
Bibliographic record
Abstract
Liquid injection in gas-solid fluidized beds is used in several industrial processes including food additive production, manufacturing of fertilizers, intermediate processing of pharmaceutical capsules and oil refining (i.e., Fluid CokingTM and Fluid Catalytic Cracking (FCC)). Good quality in the liquid distribution on the fluidized particles is fundamental in these processes either to ensure uniformity in the formation of desirable agglomerates, or to minimize their formation when undesirable. In the Fluid Coking process, for example, improving the liquid distribution and minimizing agglomerate formation will result in increasing the overall yield of the process and avoid operability problems.\nA new experimental model that simulates agglomerate formation in fluidized beds and investigates their stability was developed and tested at pilot plant scale. The model can be adapted to model different processes, but, in this work, Fluid CokingTM was selected as an example of a system to model. The liquid content of the agglomerates was estimated with a new, simple and accurate procedure that can be applied to the whole mass of the recovered agglomerates. This procedure was extended to estimate the recoating of individual agglomerates by different sprays resulting from multiple feed jets.\nThe results indicated that reducing the average bed temperature or the mass of injected liquid (liquid load) enhanced the liquid distribution. Also, the maximum extent of liquid spread from wet to dry particles was estimated. The results indicated that the spreading is not a significant factor in reactors such as Fluid Cokers.
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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 | 0.001 | 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".