Transformer oil reclamation by combining several strategies enhanced by the use of four adsorbents
Bibliographic record
Abstract
From an environmental perspective, petroleum‐based aged oils removed from power transformers are source of several pollutants and therefore cannot be disposed of without due care. The degradation of oil in in‐service transformers is due to various factors concurrent with the operation of the units over several years. The present study proposes a new strategy to rejuvenate used mineral oils by combining centrifugation, dehydration and sorption with four different adsorbents: activated carbon (ACH), silica gel (SG), magnesium oxide (MO) and activated bentonite (AB). The process of regeneration proposed in this study resulted in a level of restoration that saw the used oil take on the characteristics of new oil (colour, dissipation factor, resistivity, permittivity, acid number). The results also showed that the optimum form of the re‐refined base oil can be attributed to a 10% (w/w) quaternary mixture of the adsorbents, itself comprised of 1% ACH, 6% SG, 1% MO and 2% AB. The anticipated benefits are reduced risk of dielectric breakdown blamed for over 75% of extra high‐voltage (EHV) power transformer failures and extended transformer life expectancy by retarding the solid insulation aging processes.
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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) | 0.001 | 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".