Synergistic Interactions Between Different Components on Blending
Bibliographic record
Abstract
Abstract Chemical demulsification is the most widely used method for breaking water-in-diluted bitumen emulsions in oil sands processing. In this paper, the properties and the performance of six intermediates and three blends were investigated. The intermediates and blends were characterized by their relative solubility number (RSN). The results showed that the RSN is an additive property. The dehydration efficiency of the blends was higher than the individual components at the same dosage, indicating there were synergistic interactions among the components on blending. The performance of the demulsifiers was correlated to the interfacial tension (IFT), yield stress of underflow, and bitumen loss to tailings. The equilibrium IFT results did not illustrate any correlation with the performance of the demulsifiers. The yield stress of the underflow, which included settled solids, water, and a rag layer increased with increasing dosage of either intermediate or blend. In addition, the bitumen loss to underflow increased with increasing dosage of either intermediate or blend. The yield stress and bitumen loss to underflow reduced on blending the intermediates. The bitumen loss to underflow increased the size of the aggregates present in the underflow, increasing their immobility and constriction to flow, and eventually a higher yield stress.
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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) | 0.001 | 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".