Catalytic Cracking Reaction of Heavy Oil in the Presence of Cerium Oxide Nanoparticles in Supercritical Water
Bibliographic record
Abstract
Catalytic cracking of Canadian oil sand bitumen in supercritical water was performed to clarify the effect of CeO 2 nanoparticles. The cracking was performed at 723 K to promote a redox reaction between the water, bitumen, and catalyst for the production of hydrogen and oxygen. As the catalyst, CeO 2 with two different morphologies was employed because the redox reaction of CeO 2 with water and organics is expected and its activity can be controlled by its structure. In this study, two roles of water were considered as well. Water is attractive as a high potential medium with low dielectric constant and density at near the critical point (374 °C, 22.1 MPa) that allows formation of highly crystalline smaller metal oxides particles. However, the chemical effects of water are investigated with heavy oil catalytic cracking. Transmission electron microscopy images indicated that CeO 2 nanoparticles with cubic and octahedral shape were synthesized using a plug-flow reactor under hydrothermal conditions. The particles sizes were 8 and 50 nm for cubic and octahedral CeO 2, respectively. At 773 K, it was found that the oxygen storage capacity (OSC) of the cerium oxide nanoparticles with cubic {100} facets was nearly 3.4 times higher than that of the cerium oxide nanoparticles with octahedral {111} facets. Heavy oil fractions of bitumen were cracked in a batch-type reactor at 723 K in order to produce as much light oil as possible, and the effect of the catalyst loading and reaction conditions on the conversion rate and coke formation were investigated. As a result, it was demonstrated that it is possible to obtain a high conversion rate by increasing the exposed surface area and reducing the particle size of the catalyst. The highest conversion was obtained in the presence of 20 mg loading of cubic CeO 2 nanoparticles (8 nm) with reaction time of 1 h.
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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".