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Record W4289767443 · doi:10.25077/jnte.v11n2.968.2022

Gas Production by Monoester of Saturated Fatty Acids under Electrical Fault

2022· article· en· W4289767443 on OpenAlexaff
Abdul Rajab, Dzaki Ahmad Fertoni, Riko Novendra, Husnul Fajri

Bibliographic record

VenueJurnal Nasional Teknik Elektro · 2022
Typearticle
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsPositive Living North
FundersUniversitas Andalas
KeywordsGas chromatographyFossil fuelVoltageHydrocarbonFault (geology)Petroleum engineeringChromatographyElectric potential energyHigh voltageChemistryEnvironmental sciencePulp and paper industryAnalytical Chemistry (journal)Materials scienceEnergy (signal processing)Organic chemistryGeologyElectrical engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

This paper deals with the gas production by monoester oil intended to be used as insulating oil under an electrical discharge of low energy. The monoester contains only saturated fatty acids in its hydrocarbon chain. The electrical fault was realized by implementing an AC high voltage to hemispherical shaped electrode pairs with the gap of 2.5 mm immersed in the oil sample. The voltage application was paused when the breakdown occurred in oil and re-applied repeatedly up to 50 and 75 times to allow a high concentration of gasses produced by the oil sample. The resulting gasses were extracted from the oil sample using the headspace method and then analyzed using gas chromatography (GC). Fault identification methods, like DGA status, Key Gas, Duval Triangle, and IEC Ratio, were performed to predict the fault causing the production of such gasses. The results are compared with those of the monoester of unsaturated type. It is found that the Key Gas method is applicable for both oils under electrical discharge. The Duval Triangle and the IEC Ratio methods diagnose the electrical discharge in both monoesters but overestimate them as high energy discharge.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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