Interactions Between Supersonic Gas Jets and Gas-Solid Fluidized Beds
Bibliographic record
Abstract
This thesis presents research on the use of attrition nozzles in gas-solid fluidized beds for two applications: green sand reclamation and particle size control in fluid cokers. These two applications make use of the same attrition principles, and the main difference between them is that for green sand reclamation fragmentation is not desired, since its goal is just to remove an outer layer of the green sand particles. The green sand reclamation method proposed in this research is a new, innovative process while jet attrition is already used in fluid cokers. In addition to fluid cokers, other applications of\njet attrition in fluidized beds are in processes for the production of agricultural products, chemicals, carbon black, ceramics, minerals and metals, pharmaceuticals, cosmetics, pigments, resins and toners.\nIn the green sand experiments, the main objective was to determine the technical and economical feasibility of green sand reclamation using jet attrition in a gas-solid fluidized bed, and experimental results show that this proposed reclamation method may be technically and economically feasible if the green sand is calcinated prior to attrition.\nIn the particle size control experiments, the objective was to optimize the attrition nozzles that can be used in the fluid coking process to control the particle size distribution of the coke particles in order to maintain good fluidization. The main objective was to reduce the consumption of attrition gas. These nozzles are Laval type or convergent-divergent nozzles, and when modeling them, it was found that the original attrition nozzle has a geometrical shape that can be considered close to an optimum geometry. In addition to this, results show that grinding efficiency is correlated to the thrust produced by the supersonic nozzles.\nPenetration of the gas jet created by the supersonic nozzles was studied using a set of triboelectric probes, and two correlations were presented: one that relates thrust with jet penetration, and another one that is a modification of a correlation that was proposed by Benjelloun et al. for the penetration of subsonic jets. These correlations agreed very well with the experimental data. Jet penetration studies are useful to avoid erosion of bed\ninternals.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".