Optimizing the Impact of Pour Point Depressants on Natural Ester Properties Using Taguchi-Grey Relational Analysis
Bibliographic record
Abstract
The global surge in green-insulating liquid adoption by manufacturers and utilities is driven by the escalating demand for sustainable and environmentally friendly energy sources. Natural esters stand out as a green insulating liquid, an alternative to mineral insulating oil. However, natural esters encounter pour point challenges, particularly in cold regions. This research aims to enhance the performance of natural ester-insulating liquids, contributing to the evolution of sustainable energy technologies. In this study, optimization of pour point depressants’ impact on crucial oil properties like dielectric loss, viscosity, and acidity is investigated. Viscoplex 10-171 and Viscoplex 10-312 depressants were optimized using Taguchi methodology aided by grey relation analysis. Experimentation involved five levels 0.6 wt.%, 0.7 wt.%, 0.8 wt.%, 0.9 wt.%, and 1 wt.%, structured within an L25 orthogonal array to determine the most effective concentration. The optimization findings suggest that incorporating the two depressants does not exert notable effects on the oil properties, with all experimental values aligning within the specified ASTM standard.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".