Demulsifier Performance in Diluted Bitumen Dewatering: Effects of Mixing and Demulsifier Dosage
Bibliographic record
Abstract
Mixing conditions were explored as a possible avenue for improvement of demulsifier performance in the solvent-diluted bitumen dewatering process. The effects of demulsifier bulk concentration, demulsifier injection concentration, and mixing energy on water and solids removal from the oil phase were tested. All of the experiments were carried out in a confined impeller stirred tank, which provides well-characterized mixing conditions and relatively uniform flow and turbulence. Results showed that lowering the injection concentration and increasing the mixing energy both improve demulsifier performance, allowing a 50% drop in the bulk concentration of demulsifier. This result agrees well with an earlier study by Laplante et al.1 in which a different demulsifier was investigated. In that study, it was shown that the product of mixing time and energy dissipation rate at the feed point (the mixing energy = J ) provides an alternate mixing variable.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".