Effect of steaming treatment in the structure and reactivity of FCC catalysts
Bibliographic record
Abstract
Abstract The shape selectivity properties of USY zeolite crystallites is discussed, and is based on catalyst characterization, molecular simulation, catalytic experiments of model compounds and kinetic modeling. Typical FCC catalysts are prepared with different HY crystallite sizes (0.4 and 0.9 μm), and are structurally and chemically characterized with nitrogen and argon adsorption/desorption isotherms, temperature‐programmed desorption of ammonia and infrared spectroscopy. Catalyst characterization is carried out before and after the hydrothermal treatment (steaming) of the catalyst. Pore‐size distribution analysis demonstrates that the effect of the steaming treatment in the Y zeolite results in window enlargement. The influences of structural changes of steam treatment on reactivity is evaluated with the catalytic conversion of 1,2,4‐trimethylbenzene in a novel fluidized CREC riser simulator. It is proven that steaming enlarges zeolite windows and influence the 1,2,4‐TMB product distribution. A slight modification of the window diameter is proven to significantly affect the adsorbent‐adsorbate interactions. Focus is particularly given to the catalyst selectivity toward the tetramethylbenzene isomers, and the “transition‐state shape selectivity” is proven to be controlling the product distribution and is consistent with molecular mechanics calculations. © 2005 American Institute of Chemical Engineers AIChE J, 2006
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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 | 0.000 | 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) | 0.001 | 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".