Roles of Nanostructured Bimetallic Supported on Alumina-Zeolite (AZ) in Light Cycle Oil (LCO) Upgrading
Bibliographic record
Abstract
Light cycle oil (LCO) is one of the major products in Fluid catalytic cracking (FCC) processes, and has drawbacks such as high aromatics, sulfur, and nitrogen contents, and low cetane number (CN). Hydro-upgrading is one of the most typical processes for LCO upgrading, and alumina-zeolite (AZ) is an effective hydrotreating catalyst support. This paper examined the effects of different bimetallic catalysts (CoMo/AZ, NiMo/AZ, and NiW/AZ) supported by AZ on hydro-upgrading of both model compounds and real LCO. CoMo/AZ preferred the direct desulfurization (DDS) route while the NiMo/AZ and NiW/AZ catalysts favored the desulfurization route through hydrogenation (HYD). The presence of nitrogen compounds in the feed introduced a competitive adsorption mechanism and reduced the number of available acid sites. Aromatics were partially hydrogenated into methyltetralines at first, and then further hydrogenated, cracked, and isomerized into methyldecalins, monocyclic, and methyltetralines isomers. CoMo/AZ is the best hydrodesulfurization (HDS) catalyst for the model compounds at low H2 pressure (550 psi) and for LCO at lower temperature (573 K), while NiMo/AZ performs the best for LCO at higher temperature (648 K). NiMo/AZ is the best hydrodenitrogenation (HDN) catalyst for LCO. The hydrodearomatization (HDA) performances of NiMo/AZ and NiW/AZ improved significantly and overwhelmingly higher than that of the CoMo/AZ when the H2 pressure was increased to 1100 psi.
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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 | 0.001 |
| 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) | 0.000 | 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".