Characteristics of zeolite‐modified NiMo/Al<sub>2</sub>O<sub>3</sub> catalysts and their hydrotreating performance for light cycled oil
Bibliographic record
Abstract
Abstract The performance of a widely used commercial catalyst, NiMo/Al2O3, might be not adequate to meet more stringent regulations for the fuel products; therefore, a lot of research has been devoted to improving its performance. In this study, three different modified zeolites were added in NiMo/Al2O3 catalyst to investigate the selective improvement of the hydrodesulphurization (HDS) and hydrodenitrification (HDN) activity. The catalysts were characterized by N2 adsorption, pyridine‐infrared spectroscopy (IR), H2‐temperature program reduction (H2‐TPR), x‐ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM), and their catalytic activity was tested in the HDS and HDN of light cycle oil (LCO). Characterization results revealed that the catalyst surface area increased but the pore volume and average pore size decreased by adding modified zeolite. The activity evaluation result showed that the NiMo/MB catalyst had the highest activity and meanwhile decreased the final boiling point from 520.8 to 457 °C. The sulphur decreased by 97.65 % from 13 000 to 305 ppm and the nitrogen reduced by 99.74 % from 496 to 1.29 ppm in LCO hydrotreating, notably for NiMo/MB catalyst. The acid properties indicated that zeolite brought some strong and ultra‐strong acidic sites, which might favour a cracking reaction and reduce the liquid recovery yield. However, only 1.99 and 1.34 wt% of the LCO were cracked over the zeolite beta and zeolite modified beta respectively, indicating a slight cracking process. These results suggest that an added modified beta not only raised the quality of LCO distillation but also avoided the overcracking of hydrocarbons.
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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) | 0.001 | 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".