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Record W4408423385 · doi:10.1109/tdei.2025.3551678

Reclamation of Synthetic Ester Dielectric Liquids by Pressure and Gravity Percolation Methods

2025· article· en· W4408423385 on OpenAlexafffund
T. Jayasree, I. Fofana, Esperanza Mariela Rodriguez-Celis, Patrick Picher, Stephan Brettschneider

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of CanadaHydro-Québec
KeywordsLand reclamationPercolation (cognitive psychology)DielectricMaterials sciencePetroleum engineeringEnvironmental scienceGeologyOptoelectronics

Abstract

fetched live from OpenAlex

The tremendous demand for electricity requires higher operating voltages, which in turn impose great stress on the transformer insulation system. Condition-based maintenance and liquid decontamination are activities for assessing the risks and extending the service life of such systems. It is therefore important to explore the reclamation of the new biodegradable liquids. It is known that adsorbent-based reclamation is a prominent approach to treating insulating liquids. The present article provides the results of an experimental study on the reclamation of two low-pour-point synthetic ester fluids and a typical synthetic ester liquid using magnesium silicate-based adsorbents. The experimental results also include mineral insulating oil for a baseline reference. All four of these liquids were subjected to accelerated aging under open beaker conditions in the presence of cellulose. The aged liquids were then regenerated by pressure and gravity percolations with two magnesium silicate-based adsorbents. Physicochemical and electrical characterizations were conducted on both the feed and the filtrate. It is inferred that magnesium silicate-based adsorbents have some potential for the reclamation of synthetic esters by removing the polar compounds that evolved with the liquid’s service life.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.275
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Dielectrics and Electrical InsulationSame topicIonic liquids properties and applicationsFrench-language works237,207