Solvent removal from cyclohexane‐extracted oil sands gangue
Bibliographic record
Abstract
Abstract Solvent extraction of bitumen from the Alberta oil sands has the potential to overcome the shortcomings of the current aqueous extraction process by reducing fresh water demand and eliminating tailings ponds. Bitumen is recovered as a solution, leaving behind the solids (or gangue) with a residual solvent and water from the original ore. Removing the solvent from the gangue is a crucial step that involves evaporation of volatile material (cyclohexane) from a porous matrix in the presence of a less volatile immiscible phase (water). In this study, we examined the effects of relative humidity, temperature, and the addition of water during cyclohexane extraction on the extent of cyclohexane removal from the extraction gangue. The drying curves showed two constant‐rate periods, with the initial period mainly removing cyclohexane, and the second mainly removing water. The initial drying rate of gangue in an environmental chamber was 2.7 times faster than drying in a fume hood due to the stronger convective currents in the chamber produced by its fan. The initial drying rate was independent of the relative humidity, which was in the range of 30–90 %, but the residual cyclohexane content in the gangue increased at high relative humidity. The addition of water to oil sands prior to cyclohexane extraction increased the residual cyclohexane in the gangue, likely by forming a barrier to efficient evaporation.
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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".