INVESTIGATION ON ENHANCED MICROWAVE DEMULSIFICATION USING INORGANIC SALTS
Bibliographic record
Abstract
While the formation of water-in-crude oil (W/O) emulsion is identified to cause serious problems in petroleum industry such as decreasing the oil recovery efficiency, increasing pumping cost, and generating pipeline corrosion, much of its treatment has been focused mostly on gravity separation by mechanical/chemical technology. As an integral part in the process of oil production and transportation, crude oil demulsification has become a significant research attention. Microwave has also long been employed to crude oil demulsification based on the fact that microwave heating can dissipate heat in aqueous medium and raise the energy of the molecules rapidly. However, the research on salts-assisted microwave demulsification has been limited to salts mostly identified in produce-water. The aim of this work is to carry out a comprehensive research on the effect of microwave demulsification and the influence of a variety of inorganic salts to the microwave process. Also, a comparative study on microwave demulsification between heavy oil and light oil were conducted. The obtained results revealed water separation of 47% between irradiated and non-irradiated emulsion for heavy crude oil compared to 13% for light crude oil. Also the effect of different inorganic salts and its optimum amount to promote demulsification were identified.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".