Bibliographic record
Abstract
A major operational issue in the crude oil industry is the formation of intermediate rag layers, (primarily water-in-oil emulsions) in oil−water separation processes that limit the amount and quality of recoverable oil. In this study, the formation of rag layers is evaluated as a function of solvent−bitumen−water ratios, solvent aromaticity, and temperature, with various imaging techniques. Using these techniques, it is possible to obtain an estimate of the amount of oil, water, and asphalthenes in the rag layer and excess phases. On the basis of these material balances, it was observed that when bitumen is diluted with a more paraffinic (poor) solvent, such as Heptol 80/20 (80% heptane and 20% toluene), the asphaltenes in solution tend to adsorb/segregate at exposed oil−water interfaces, impacting the extent of rag layer formation. Diluting similar systems with a more aromatic solvent (Heptol 50/50) reduces the surface activity of the asphaltenes, and the stability of rag layers, as evidenced by lower asphaltene and oil losses to the rag layer. Furthermore, it was observed that increasing the temperature of the system minimizes rag layer formation and the fraction of oil lost to the rag layer. The better separation at high temperature could be explained by the lower viscosity of the oil, which results in improved oil drainage from the rag layer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".