Towards the development of cavitation technology for upgrading bitumen: Viscosity change and chemical cavitation yield measurements
Bibliographic record
Abstract
Among the different methods used for reducing viscosity of bitumen, acoustic cavitation during sonication is well recognised. Several chemical methods were used to detect the production of reactive species such as hydroxyl radicals and hydrogen peroxide during acoustic cavitation processes. However, quantification of cavitation yield in sonochemical systems is generally limited to low frequencies and has not been applied to bitumen processing. An empirical determination of the cavitation yield in mid- to high-frequency range (378, 574, 850, 992, and 1173 kHz) was carried out by measuring the amount of iodine liberated from the oxidation of potassium iodide (KI). Further, cavitation yield and the effects of different sonic operating conditions such as power input (16.67%–83.33%) and solute concentration on cavitation yield were carried out in KI solution and sodium carboxymethyl cellulose–water mixture to obtain benchmark changes in rheology and chemistry using these two model fluids. The findings were then applied to bitumen upgrading through sonication. Through this study, it was found that the chemical cavitation yield peaked with a sonication frequency of 574 kHz. It was also found that cavitation yield and viscosity change were correlated directly in bitumen and a 38% lower bitumen viscosity could be obtained by acoustic cavitation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".