Spraying slurries in fluidized beds: Impact of slurry properties on spray characteristics and agglomerate formation
Bibliographic record
Abstract
Abstract Fluid coking is a process that upgrades heavy oil. In a fluid coker, heavy oil is sprayed into a fluidized bed of hot coke particles, where it undergoes thermal cracking. The formation of wet agglomerates, caused by poor contacting between the heavy oil and coke particles, slows thermal cracking and leads to operating problems. The product vapours exiting the coker are scrubbed to condense the heavier components, which are recycled to the coker. The recycle stream contains unwanted suspended fines that could affect the interaction between the liquid feed and the bed particles, and the resulting agglomerate formation. In open air, the presence of suspended solids had a negligible impact on spray behaviour. The impacts on agglomerate stability and liquid distribution were studied in a fluidized bed of sand at 130°C by spraying a gum arabic solution that was formulated to simulate oil‐coke agglomerate formation in fluid cokers. Within the fluidized bed, changing the concentration of suspended solids in the liquid feed affected agglomerate stability and liquid distribution. Different mechanisms were considered to explain this change in agglomeration behaviour: increased viscosity, spray characteristics, drying kinetics, and the solids filler effect. A dedicated experimental plan was designed to test each hypothesis. The only significant impact of the suspended solids resulted from a filler effect within the agglomerates: The fines made the agglomerates more resistant to breakage in the fluidized bed. This only occurred, however, when the suspended solids were wettable by the liquid; non‐wettable suspended solids, instead, weakened 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 | 0.000 |
| 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".