Bitumen heavy oil upgrading by cavitation processing: effect on asphaltene separation, rheology, and metal content
Bibliographic record
Abstract
Cavitation processing has been proposed as a greener alternative to solvent dilution or heat treatment of bitumen and other heavy oils to reduce viscosity and hence, improve transportability. The effect of acoustic cavitation under different conditions of sonication frequencies (low- to high- frequency range) and power inputs on asphaltene content, rheological changes, and metal content of bitumen was investigated in this study. Ultrasonic treatment resulted in a decrease in asphaltene content in bitumen that lead to lower viscosity and shear stress over a wide range of shear rates. Over the range of sonication frequencies investigated (20 kHz–1.1 MHz), the sonication frequency of 574 kHz with 50 % power input resulted in low asphaltene content and lower viscosity suitable for improved transportability. Further, comparison of different conditions of sonication frequencies and power inputs were carried out to investigate the effect of ultrasound on properties of asphaltene (elemental analysis and metal content). It was observed that the sonication treatment of bitumen under different conditions of frequencies and acoustic power decreased the H/C ratio. These results showed higher content of aromatic hydrogen and lower content of aliphatic hydrogen in bitumen treated under different conditions of sonication frequencies and intensity. Characterization of asphaltene performed using ICP-MS and TXRF, revealed lower metal content (Ni, Fe, and V) in the asphaltene phase of processed (sonicated) bitumen. The lowered metal content can be attributed to the reduced asphaltene formation as a result of sonication treatment of bitumen.
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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".