Reduce Overdosing Effects in Chemical Demulsifier Applications by Increasing Mixing Energy and Decreasing Injection Concentration
Bibliographic record
Abstract
It has been known for years that the performance of a demulsifier in diluted bitumen dewatering improves up to a certain demulsifier bulk concentration. After this limit, the water removal deteriorates. This phenomenon is called overdosing. In this paper, the effects of mixing energy and demulsifier injection concentration on water removal are studied in systems with a high bulk demulsifier concentration. The experiments were conducted in a confined impeller stirred tank (CIST), which provides well-controlled mixing conditions with more uniform turbulence and flow than a conventional stirred tank. The results show that an increase in mixing energy and pre-dilution of the demulsifier may be able to overcome overdosing effects at a high bulk (mean) concentration. If the mixing conditions are well-designed, high local demulsifier concentrations at the feed point are avoided and the demulsifier can perform well, even at high bulk concentrations. The best demulsifier performance in an overdosed system was obtained with a combination of high mixing energy and low injection concentration, where over 80% of the water content was separated at a demulsifier bulk concentration that showed severe overdosing behavior at poor mixing conditions.
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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 | 0.001 |
| 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 | 0.001 |
| 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".