Electrostatic field as an emergent technology in refining crude oils: a review
Bibliographic record
Abstract
Vegetable oils and fatty acid esters (FAEs) are commonly used in various industrial and commercial applications. However, the presence of contaminants in these oils can severely affect their functionality and suitability. Conventional refining techniques for vegetable oils typically involve degumming, neutralization, bleaching and deodorization. Meanwhile, refining of FAEs often utilize wet or dry washing processes. These are often resource-intensive, producing substantial waste products, causing neutral oil loss, and can also result in the loss of micronutrients. To address these challenges, researchers have explored the use of nano-adsorbents and electrostatic field (E-field) technologies as alternatives in purifying industrial dielectric oils by removing polar particles and contaminants. Nano-adsorbents demonstrated increased efficiency in removing polar contamination while minimizing neutral oil loss. However, removal of these spent adsorbents can be challenging due to their nano-size, and physicochemical properties. The use of these materials combined with E-field technologies offers a novel and sustainable solution for removing spent nano-adsorbents and contaminants. This review provides an overview of current traditional and novel refining technologies for vegetable oils and FAEs, including their associated limitations. Compared to conventional methods, E-field treatment offers several advantages, making it an attractive alternative to conventional approaches in food processing and oil refining.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".