Understanding ethanol versus methanol formation from insulating paper in power transformers
Bibliographic record
Abstract
Abstract The life of an electrical transformer is mainly determined by that of its cellulosic solid insulation. The analysis of the chemical markers of cellulose degradation dissolved in oil is a simple and economical way to indirectly characterize the insulating paper. Methanol, a marker that is intimately linked to the rupturing of 1,4-β-glycosidic bonds of cellulose, has been observed together with ethanol during laboratory ageing experiments. Regardless of the simulated ageing conditions (temperature, humidity, air), the ratio of methanol to ethanol concentration is always higher than one (unity). However, in approximately 10 % of transformer oil samples, the ethanol generation is higher than that of methanol. In this study, thermal degradation by pyrolysis is coupled with gas chromatography/mass spectrometry to assess the volatile by-products generated at high temperatures with emphasis on methanol/ethanol generation. Some cellulose model compounds were also pyrolyzed and thermally aged in oil. The results showed that the generation of ethanol from paper pyrolysis is always smaller than for methanol, but it only occurs at temperatures higher than 300 °C. However, thermal ageing of levoglucosan in oil generates a massive amount of ethanol compared to methanol regardless of the conditions (temperature, humidity, air, nitrogen, acidity). The hypothesis that ethanol is a by-product of cellulose degradation through levoglucosan as an intermediary in power transformers is proposed. The presence of ethanol during transformer oil analysis is of high interest because it can be related to a thermal fault or hot spot within the solid insulation.
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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 | 0.001 | 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".