Microwave-Assisted Remediation of Nonaqueous Oil Sand Gangue: Effects of Solvent, Fines, and Water Content
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Microwave heating (MWH) has been identified as a possible solution to remediate nonaqueous extraction (NAE) oil sand gangue due to its selective and rapid heating nature. This work investigated the use of MWH to remove solvent from NAE-reconstituted gangue with different concentrations of cyclohexane, fine particles, and water (MWH susceptor). The results showed that the water/cyclohexane mass ratio needed to perform solvent removal decreases with either an increase in the fine particle content or a decrease in the cyclohexane concentration. The gangue drying rate exhibited very good agreement with a simple Lewis exponential drying model. The drying curves indicated that regardless of the initial cyclohexane concentration, a higher fine content results in a shorter vaporization time and a faster solvent removal rate. This was attributed to the larger surface area of the fines, which facilitates the contact between water and the gangue sample, thereby improving the heat transfer and promoting cyclohexane removal. These results demonstrate a 3-fold improvement compared with the conventional heating of NAE gangue from a low-grade ore. Overall, the findings show that MWH can effectively remediate NAE gangue across different sample compositions, addressing economic and environmental constraints. This lays the foundation for the development of a commercial NAE technology and supports the efforts of the oil sand industry to achieve net-zero emissions.
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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".