Application of Novel Zeolite Y Nanoparticles in Catalytic Cracking Reactions
Bibliographic record
Abstract
The cracking activity of a fluid catalytic cracking (FCC) catalyst containing novel zeolite Y nanoparticles fabricated using mesoporous silica (average particle size of 150 nm) was examined and compared with the performance of other catalysts. The activity experiments were carried out in a fluidized bench-scale batch riser simulator reactor. The bulky probing compound of 1,3,5-triisopropylbenzene (TIPB) was cracked to lighter compounds over a catalyst containing 25% of the developed zeolite. The synthesized sodium-type zeolite nanoparticles were subjected to two cycles of ion-exchange treatment using ammonium sulfate and lanthanum chloride and then to calcination. To investigate the effects of particle size on the activity, three additional catalysts were prepared with the mean particle size of the supported zeolites ranging from 450 to 1800 nm. The preparation of the FCC catalysts was conducted by mixing the highly aqueous dispersed zeolite Y nanoparticles with colloidal silica–alumina as a matrix and silica sol as a binder. The results of the catalytic cracking of TIPB demonstrated the significant effect of the size reduction of the synthesized zeolite Y nanoparticles on the catalytic performance of the catalyst. The FCC catalyst that contained zeolite Y nanoparticles (150 nm) showed superior conversion and selectivity percentages for the main products. The results of this study have a direct implication on the preparation of colloidal catalysts containing zeolite Y nanoparticles, which form stable emulsion with petroleum products. These emulsions can be utilized for slurry and ebullated bed reactors in heavy oil upgrading applications.
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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 | 0.000 | 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 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".