Enhancing Oxidation Stability of Natural Esters: A Multiresponse Statistical Analysis Approach
Bibliographic record
Abstract
The exploration of natural esters as green insulating liquids has gained significant attention over the past two decades due to their exceptional qualities for power transformers. However, due to their chemical compositions, the fatty acids make them more susceptible to oxidation compared to mineral oils, highlighting the need for improvements. This study explores the thermo-oxidative enhancement of two natural esters, NE1 and NE2, through the incorporation of tert-butylhydroquinone (TBHQ) and citric acid (CA) antioxidants. The research proceeds in two phases. In the first phase, oxidation stability is evaluated using a full factorial design to analyze the target responses, dissipation factor, acidity, and viscosity through standard methods. The results, assessed through analysis of variance (ANOVA), identify optimal antioxidant concentrations of 0.2 wt.% TBHQ and CA for NE1, and 0.2 wt.% TBHQ with 0.15 wt.% CA for NE2. TBHQ primarily influences viscosity and acidity, while CA notably impacts the dissipation factor. In the second phase, accelerated thermal aging tests are conducted to evaluate the performance of the oils under thermal stress. The antioxidant-treated samples demonstrate significant improvements in oxidative stability, with NE1 showing only an 11.45% increase in viscosity, compared to a 20% increase in NE2 after 1500 hours of aging. These results highlight the importance of tailoring antioxidant formulations to the chemical structure of natural esters, supporting their broader application in transformer 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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".