Impact of Local Bed Hydrodynamics on Jet-Bed Interaction
Bibliographic record
Abstract
In Fluid CokingTM or Fluid Catalytic Cracking liquid feedstocks are injected into a bed of fluidized particles. Uniform distribution of liquid feed on fluidized particles increases the yield of valuable products and improves operability in these processes. Contact between the injected liquid and the bed particles can be greatly affected by the liquid properties and local bed hydrodynamics.\nThe impact of parameters such as liquid properties, fluidization velocity, nozzle atomization gas flowrate, nozzle location and inclination were investigated on the distribution of liquid sprayed into a fluidized bed with a reliable and fast response capacitance meter. This method was also extended to monitor the agglomerate breakup kinetics.\nThe research showed that a liquid whose viscosity and contact angle on the surface of solid particles are similar to the liquid used in the high temperature commercial reactors provides a good simulation of liquid distribution into a fluidized bed with room temperature experiments. VarsolTM was selected for a cold simulation of Fluid Cokers.\nThe research also presents an innovative design of a cold model fluidized bed to investigate the impact of the velocity of particles, relative to the spray nozzle, on solid-liquid contact since it varies greatly with location in actual Fluid Cokers. This design provided an inexpensive and accessible means to study the effect of the relative velocity between the spray jet and particles independently of other bed hydrodynamic characteristics.\nThe investigation found that distribution of the injected liquid in the bed can be improved by either increasing the atomization gas flowrate or, preferably, the fluidization velocity. Study of nozzles at different locations and inclinations identified the dominant effects of bed hydrodynamics at the nozzle and jet tips on the distribution of liquid on solid particles.\nA model for the interactions between sprayed liquid and fluidized particles was developed for two cases: a) a stationary spray nozzle and no net motion of the fluidized solids and b) a moving nozzle with a relative velocity between spray nozzle and particles. The model results were compared with experimental results and were found to provide consistent information on the liquid concentration of the agglomerates.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".