Heavy oil upgrading in a hydrodynamic cavitation system: CFD modelling, effect of the presence of hydrogen donor and metal nanoparticles
Bibliographic record
Abstract
The cavitation technique with the use of a proper hydrogen donor can be used to upgrade heavy oils, such as atmospheric and vacuum residues and lower the difficulties of their transportation and exploitation. On the other hand, the very high localized temperature experienced in collapsing bubbles may activate the dispersed metal nanoparticles, and therefore the cracking or hydrogenation reactions may be catalyzed through the cavitation process of heavy oils. This paper investigated numerically the formation of a vapour phase in the cavitation chamber of a home‐made laboratory hydrodynamic cavitation setup and also investigated the upgrading of a sample of heavy fuel oil in the presence of gasoline as a hydrogen donor and metal nanoparticles. The results indicated that adding 0.01 L/L gasoline to a 10‐min cavitational cracking process at 80 °C and atmospheric pressure can reduce the viscosity of heavy oil by about 20 %. In addition, the presence of iron nanoparticles can increase the rate of hydrogenation and/or cracking reactions in the heavy oil cavitational upgrading process (HCUP) in the presence of a hydrogen donor.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".