Destabilization of bitumen-coated fine solids in oil through water-assisted flocculation using biomolecules extracted from guar beans
Bibliographic record
Abstract
Abstract Non-aqueous extraction (NAE) of bitumen from oil sands has been gaining great attention from both the industry and academia as an alternative to the water-based extraction. A fine solids removal step is important for a NAE process in order to obtain high-quality bitumen product, which, however, remains a great challenge to reduce the fine solids content to the desired level. Here, we introduce a strategy of destabilizing the bitumen-coated silica particles in toluene with the addition of water and biomolecules extracted from Cyamopsiste tragonolobuosr L. Taup., i.e., high molecular weight guar gum (HGG) and low molecular weight guar gum (LGG), respectively. By virtue of sedimentation tests and focused beam reflectance measurement analysis, we demonstrate that the introduced water droplets modified with these biomolecules can facilitate the settling of the solid particles in toluene although the underlying mechanisms differ between these two biomolecule cases. Specifically, in the case of LGG, the added water droplets with the interfacial amphiphilic LGG can strengthen the attachment of solid particles from bulk toluene to the LGG surface. This research work provides useful insight into the development of effective approaches for destabilization and removal of bitumen-coated fine solids from NAE bitumen.
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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) | 0.000 | 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".