Investigation of liquid distribution in gas–solid fluidized beds for fluid cokers
Bibliographic record
Abstract
Abstract Thermal cracking processes convert larger molecules into smaller, more valuable products, without a catalyst, allowing for transforming residual oils or waste plastics into useful compounds. Fluid coking, a thermal cracking process utilizing a fluidized bed of hot particles, processes approximately 1 million barrels of residual oil daily. This study aims to understand the formation and breakage of wet agglomerates in fluidized beds, which are known to impact the efficiency of thermal cracking by promoting coke formation and fouling. A model is proposed to predict wet agglomerate formation, drying, and breakage. Experiments in a scaled‐down cold model of the reactor provided data to validate the model. The study investigated the effects of spray nozzle penetration and the addition of a baffle on agglomerate behaviour. Results indicate that increased nozzle penetration reduces wet agglomerate formation, and adding a baffle increases agglomerate drying time and promotes breakage, reducing the amount of liquid reaching the reactor outlet. The combined approach of optimizing nozzle penetration and adding a baffle significantly improves fluid coker operation by minimizing the detrimental impact of wet 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 | 0.000 |
| 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 | 0.000 |
| 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".