Role of Ethyl Cellulose in Bitumen Extraction from Oil Sands Ores Using an Aqueous–Nonaqueous Hybrid Process
Bibliographic record
Abstract
A major drawback associated with current hot or warm water-based bitumen extraction processes is the high consumption of energy. To address this issue, an aqueous–nonaqueous hybrid bitumen extraction process (HBEP), in which a portion of the diluent (solvent) was added upfront to soak mined oil sands prior to its water-based extraction, was proposed and demonstrated to be feasible to process mineable oil sands at ambient temperatures. This study investigates the effect of adding ethyl cellulose (EC) as a promising demulsifier to the solvent on bitumen recovery and froth quality in the ambient HBEP. The laboratory flotation results clearly showed a significant improvement in froth quality with a negligible setback on bitumen recovery by 100–200 ppm EC addition to the HBEP. Determined by an online visualization method, the addition of EC in solvent to the HBEP was found to further enhance separation kinetics of bitumen from sand grains of real oil sands ores. The addition of EC in solvent also increased the probability of bitumen droplet coalescence determined with a micropipette technique, but hindered the attachment of air bubbles to solvent-soaked bitumen, in particular at high EC dosages as evaluated by increased induction time of air bubble-bitumen attachment.
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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".