Simulation of Cold Flow FCC Stripper Hydrodynamics at Small Scale Using Computational Fluid Dynamics
Bibliographic record
Abstract
The refining industry trend toward increased throughput of heavier feeds, accompanied by increased catalyst circulation rates in the fluid catalytic cracking (FCC) unit, has created flow problems in the FCC stripper. This has reduced stripper efficiency, leading to loss of valuable product to the regenerator. This adversely affects the FCC unit heat balance in some cases, requiring the catalyst circulation rate to be reduced, thereby limiting the flow of feed to the unit. Research on FCC strippers published in the open literature is scarce. The purpose of the present study is to model the hydrodynamics of a cold flow, laboratory scale FCC stripper using computational fluid dynamics (CFD). Simulations were performed using the two-fluid CFD code MFIX (www.mfix.org) for operating conditions of gas superficial velocities between 0.1 and 0.33 m/s and solid circulation fluxes ranging between 28 and 90 kg/m2s. A 2-D cold-flow annular FCC stripper was simulated since this study is a first step in modeling stripper hydrodynamics. The simulations were in good qualitative and quantitative agreement with the limited cold-flow stripper hydrodynamic data published in the open literature. The model was also shown to predict the onset of flooding in the baffled stripper, and the addition of downcomers to the baffles widened the stable operating envelope.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".