Three‐dimensional CFD model of the deaeration rate of FCC particles
Bibliographic record
Abstract
Abstract Computational fluid dynamics (CFD) has been used to model the deaeration rate for a fluidized bed of fluid catalytic cracking (FCC) catalyst. The Eulerian approach has been used in which the gas and solid phases present in the fluidized bed are treated as interpenetrating continua with constitutive equations obtained from the granular kinetic theory and the interphase drag relationship. Using the CFD code MFIX, transient calculations have been made for a three‐dimensional (3‐D) cylindrical vessel of 0.104 m diameter. Various cases have been modeled: fluidization at superficial gas velocities of 0.005 and 0.028 m/s followed by deaeration for two catalyst bed masses of 3.17 and 4.05 kg. These have been performed for monosize particles of Sauter mean diameters of 69.8 and 100 microns. The results have been presented in terms of variation of gauge pressure with time at a height of 30 cm above the distributor plate. Comparisons of these predictions with the experimental data show that the deviation between the experimental results and the CFD predictions of deaeration rate is <10%. Based on these results it appears that our CFD modeling approach, which includes the introduction of an empirical correction factor for the gas–solid drag term, can adequately predict the deaeration rate of FCC catalyst using a single mean particle size. © 2006 American Institute of Chemical Engineers AIChE J, 2006
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".