Wettability alteration of solid surface to enhance the bitumen liberation and the water‐based processability of weathered oil sands
Bibliographic record
Abstract
Abstract Weathering often induces bituminous materials adsorbing on the sand grains and leads to poor processability of the oil sands. Chemical and microbial pre‐treatment of the prepared weathered ore model and a weathered oil sand ore were carried out to improve the solids surface wettability so as to facilitate the bitumen liberation and recovery. It was found that although all the cetyl trimethylammonium bromide (CTAB), sodium dodecylbenzene sulphonate (SDBS), and microbial culture medium could greatly decrease the surface tension of the solution, the CTAB treatment failed to improve the bitumen liberation, while the SDBS and microbial treatment significantly accelerated the bitumen liberation from the silicon substrates. The wettability analysis showed that the improved bitumen liberation could be attributed to the alteration of the solids surface wettability from hydrophobic to hydrophilic by the SDBS and microbial treatment. Inconsistent with the findings of the bitumen liberation, floatation tests of a weathered ore showed that the CTAB pre‐treatment only gave a low bitumen recovery of 33%, while the SDBS and microbial pre‐treatment improved the processability of the ore. In particular, the microbial treatment was more effective at removing the adsorbed organics from the solids and improved the surface hydrophilicity, resulting in a much better bitumen recovery of 95%. This work provides a way to improve the processability of the weathered ore by altering the solids surface wettability.
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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".