Effect of interactions between spray jets on liquid distribution in a fluidized bed
Bibliographic record
Abstract
Abstract In Fluid Cokers™, banks of spray nozzles are used to inject oil into a bed of hot coke particles. The purpose of this study is to determine whether interactions between spray jets could enhance liquid distribution on hot coke particles, which is crucial to improve the operability and performance of Fluid Cokers. A low temperature experimental model of Fluid Coking was used to measure the liquid distribution. Preliminary screening of nozzle positions employed conductance measurements. A binder solution was utilized to further investigate the most interesting nozzle interactions, by simulating at low temperature the formation of agglomerates during high temperature coking. Adding different dyes to the binder solutions injected by the different nozzles helped determine how nozzles interacted. With two synchronized nozzles of the same size, the liquid distribution is greatly improved when the spray jets slightly merge due to the expansion time being significantly reduced. The merged spray jets result in an unstable single jet, which allows for bubbles to be released faster from the spray jet. Because the volume of the released bubble is about the same for individual and merged jets, the jets do not contract as much upon bubble release: this enhances the jets' ability to capture gas bubbles from the bed, accelerating their expansion. Fewer agglomerates are also produced due to the improved liquid distribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".