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Record W4404363032 · doi:10.17775/cseejpes.2022.05800

Recycling of Silicone Rubber from Composite Insulator with Pyrolysis Method

2024· article· en· W4404363032 on OpenAlexaff

Bibliographic record

VenueCSEE Journal of Power and Energy Systems · 2024
Typearticle
Languageen
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNational Natural Science Foundation of China
KeywordsSilicone rubberComposite numberInsulator (electricity)Materials scienceComposite materialPyrolysisSiliconeNatural rubberProcess engineeringWaste managementEngineering

Abstract

fetched live from OpenAlex

Composite insulators have been widely used in transmission lines.After being removed from transmission lines, their housing silicone material cannot degrade naturally.To tackle this problem, this paper proposes an effective method to recycle waste insulators by pyrolysis to obtain mullite (3Al2O3•2SiO2) with high purity and compact structure.The recycling process studied will not generate toxic products.The thermal degradation process of housing material is investigated by analyzing its degradation products including the gas and residues in detail.The experimental results indicate that the colorant agent Fe2O3 inside the housing material is beneficial for the generation of mullite by decreasing the temperature of mullitization.Besides, since the transitional alumina generated by the dehydration of aluminum hydroxide (ATH) has a smaller diameter and can better dissolute into the silica phases, ATH is a better choice as the additional aluminum resource.By comparing the components, structure, and particle size of grains formed at different calcination temperatures, the proposed pyrolysis temperatures of the two stages are 1400 • C and 1600 • C, respectively.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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Same venueCSEE Journal of Power and Energy SystemsSame topicFiber-reinforced polymer compositesFrench-language works237,207