Combined removal experiment of multiple impurities from W/O emulsions under high frequency pulsed electric field: Study on laws of desalination, decalcification, and deacidification
Bibliographic record
Abstract
Abstract Electrocoalescence is widely used to dehydrate crude oil, but impurities in crude oil are extremely complex, which greatly weaken the effect of dehydration. To understand the coupling law of various impurities, a static demulsification experiment was conducted under a high frequency and high voltage pulsed electric field to investigate the effects of electric field parameters (voltage, frequency, pulse width ratio), emulsification strength, electrocoalescence time, water content, acid value, injection alkali ratio, calcium content, decalcification agent concentration and removal process on desalination, decalcification, and deacidification. The results showed that the desalination and decalcification rates varied synchronously with the electric field parameters and water content. Additionally, the desalination and decalcification rates decreased with the emulsification strength, alkali injection ratio, acid value, and calcium content. Further, they increased first and then decreased with decalcifier concentration. The deacidification rate did not vary with the electric field parameters, but it strengthened with the water content and alkali injection ratio. Conversely, it decreased with the acid value, calcium content, and decalcifier concentration. Additionally, it increased first and then decreased with the emulsification intensity. The best removal procedure is deacidification followed by decalcification. These findings are helpful to achieve the combined removal of various impurities in crude 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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".