Separation of Water from High pH Water-in-Heavy Oil Emulsions Using Low Pressure CO<sub>2</sub>
Bibliographic record
Abstract
In this study, a demulsification treatment process for high pH water-in-oil (W/O) emulsions, using low pressure CO 2 and demulsifier, with optimized temperature and mixing time is designed. The efficacy of different acids, including HCl and CH 3 COOH, are also examined using this method, to be compared with the results obtained with CO 2 gas. Both the demulsifier and CO 2 are indispensable in this demulsification treatment process. The demulsification efficiency increases rapidly with the increase of demulsifier concentration when the initial concentration is lower than 120 mg/L. With the further increase of concentration, the demulsification efficiency can reach 83% and remain constant. Meanwhile, the demulsification efficiency increases with the increase of CO 2 pressure. It is found that the optimum CO 2 pressure is between 50 and 200 kPa, with a mixing time of 65–125 min, where a demulsification efficiency of approximately 80–90% can be achieved. Settling under elevated temperature (70 °C), after CO 2 demulsification, is also investigated; the results indicate that heating does not improve the demulsification efficiency if the pressure is over 500 kPa. Under the same conditions, CO 2 shows better demulsification efficiency than that obtained by HCl and CH 3 COOH. At a pH of approximately 7.0–7.7, the emulsion is the least stable, and the maximum demulsification efficiency is obtained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".