Preparation of Fe‐based magnetic‐core‐supported Co and Ni catalyst: application in viscosity reduction of heavy oil hydrocracking
Bibliographic record
Abstract
Abstract BACKGROUND Heavy oil has the characteristics of high viscosity and low American Petroleum Institute gravity, which makes it difficult to transport and produce. The key to efficiently utilizing heavy oil is reducing viscosity and improving quality. Hydroconversion has the advantages of viscosity reduction and decreased coke production, but it is difficult to separate nonmagnetic catalysts from the reaction system, which affects the industrial application. RESULTS A spherical Fe 3 O 4 (74.71 emu g −1 ) and a chain FeFe 2 O 3 (110 emu g −1 ) were prepared as magnetic cores, and coated with SiO 2 and Al 2 O 3 layers, respectively. The metal‐supported catalyst modified with Co and Ni show irregular and honeycomb‐shaped surfaces, and the magnetic saturation strength decreases to 24.91 and 14.63 emu g −1 , but still possessing superparamagnetic properties, enabling the catalysts to be separated from the reaction mixture. Fe‐Fe 2 O 3 @SiO 2 @Al 2 O 3 @CoNi catalyst has the best viscosity reduction effect for Canadian oil sands asphalt residue at about 410 °C and 10 MPa hydrogen pressure. The viscosity of the liquid product decreased significantly from 56 600 to 135 mPa s at 50 °C, with a viscosity reduction of 99.8%. CONCLUSION Magnetic catalyst prepared from chain‐like Fe‐Fe 2 O 3 has an excellent honeycomb structure, which can provide increased active surfaces and exhibit improved viscosity reduction performance. This work provides a strategy for the recovery of heavy oil hydrogenation catalysts and is expected to play a key role in the practical application of heavy oil hydrogenation catalytic viscosity reduction. © 2023 Society of Chemical Industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".