Enhancing Bitumen Liberation by Controlling the Interfacial Tension and Viscosity Ratio through Solvent Addition
Bibliographic record
Abstract
Bitumen liberation is known to be an essential step for bitumen recovery from sand grains using the current warm/hot-water-based extraction process. This study aims at understanding the role of naphtha or toluene addition in enhancing bitumen liberation. Results from an in situ bitumen liberation visualization measurement indicate that soaking two oil sands ores by solvents at 10–30 wt % of the bitumen could significantly enhance not only the ultimate degree of bitumen liberation (UDBL) but also the rate of bitumen liberation (RBL) in the process water at ambient conditions. Although ore-type- and solvent-type-dependent, both the UDBL and RBL would increase sharply at 10–20 wt % solvent dosage. A further increase in solvent dosage showed a diminished increase in the UDBL. To understand the observed improvement, viscosities of bitumen directly extracted from the ores and its mixture with solvents were measured, as well as diluted bitumen–water interfacial tensions. Results showed that adding solvent into the bitumen reduced bitumen–water interfacial tension and more so for the reduction in bitumen viscosity. Interestingly, the viscosity and interfacial tension of diluted bitumen were found to be dependent upon the source of ores and type of solvents. The UDBL was found to be inversely correlated with the interfacial tension and bitumen viscosity, while the RBL correlated almost linearly with the interfacial tension/viscosity ratio, which acted as the balance of the interfacial tension driving force/adhesion drag force. These correlations were less dependent upon the types of ores and solvents.
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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) | 0.001 | 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".