Catalyst/Feedstock Ratio Effect on FCC Using Different Catalysts Samples
Bibliographic record
Abstract
The present study is a follow-up to a recent authors contribution which describes the effect of the C/O (catalyst/oil) ratio on catalytic cracking activity and catalyst deactivation. This study, while valuable, was limited to one fluidized catalytic cracking (FCC) catalyst. The aim of the present study is to consider the C/O effect using three FCC catalysts with different activities and acidities. Catalysts were characterized in terms of crystallinity, total acidity, specific surface Area (SSA), temperature programmed ammonia desorption (NH3-TPD), and pyridine chemisorption. 1,3,5-TIPB (1,3,5-tri-isopropyl benzene) catalytic cracking runs were carried out in a bench-scale mini-fluidized batch unit CREC (chemical reactor engineering centre) riser simulator. All data were taken at 550 °C with a contact time of 7 s. Every experiment involved 0.2 g of 1,3,5-TIPB with the amount of catalyst changing in the 0.12–1 g range. The resulting 0.6–5 g oil/g cat ratios showed a consistent 1,3,5-TIPB conversion increasing first, then stabilizing, and finally decreasing modestly. On the other hand, coke formation and undesirable benzene selectivity always rose. Thus, the reported results show that catalyst density affects both catalyst coking and deactivation, displaying an optimum C/O ratio, achieving maximum hydrocarbon conversions in FCC units.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".