Effect of ultrasonic homogenization on crude oil-water emulsion stability
Bibliographic record
Abstract
This research aims to evaluate the effect of ultrasonic processing parameters (power and sonication time), emulsion characteristics (water salinity and pH) and their interaction on oil-in-water emulsion stability for Cold Lake Blend (CLB) crude oil. Response surface methodology was used to design experimental runs, in which the parameters were investigated at five levels. Emulsion stability was evaluated by measuring creaming index, emulsion turbidity and microscopic image analysis. The effect of crude oil condition (fresh and weathered) on the emulsion stability was also investigated at the optimum sonication parameters and emulsion characteristics. The optimum condition was found at a power level of 76-80 W, sonication time of 16 mins, water salinity of 15 g/L NaCl, and pH of 8.3. Increasing sonication time beyond the optimum value had adverse effect on the emulsion stability. High water salinity (> 20 g/L NaCl) and pH (> 9) decreased the emulsion stability. These adverse effects intensified at higher power levels (> 80-87 W) and longer sonication times (> 16 mins). Interaction of parameters showed that the required energy to generate stable emulsion was within 60 - 70 kJ. Emulsion with fresh crude oil was more stable than those generated with the weathered oil.
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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 | 0.001 | 0.001 |
| 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".