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Record W3148022360 · doi:10.1002/cjce.24123

Kinetic study of pyrolysis of high‐density polyethylene (HDPE) waste at different bed thickness in a fixed bed reactor

2021· article· en· W3148022360 on OpenAlexvenueno aff
Haoyu Li, Ondřej Mašek, Alan Harper, Raffaella Ocone

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsHigh-density polyethyleneWaxPyrolysisMaterials sciencePolyethyleneThermogravimetric analysisComposite materialActivation energyCrackingChemical engineeringChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The aim of this study is to investigate the kinetic characteristics of the high‐density polyethylene (HDPE) waste pyrolysis process based on thermogravimetric analysis (TGA) and using a fixed bed pyrolytic reactor (FBPR) at different temperatures. In addition, the influence of material bed thickness on the yield distribution and the composition of products was examined over a temperature range of 425–550°C. A higher wax fraction was obtained in the thin bed of the FBPR bed at 425°C. With temperature above 500°C, more oil and wax products were generated in the thick bed of the FBPR. Based on the experimental study, a discrete first‐order kinetic lumping model, comprising three independent parallel reactions (lumps), was developed to describe the yield distribution of gases, oil fractions, wax fractions, and solid residue, coupling with secondary cracking reactions of the wax fractions into lighter fractions (i.e., oil and gas). The overall apparent activation energy (E a ) and pre‐exponential factor (A 0 ) of HDPE pyrolysis were estimated in the FBPR. The results showed that the thickness of the bed of plastics has a pronounced influence on the pyrolysis kinetics of HDPE.

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 score0.002
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
GPT teacher head0.176
Teacher spread0.169 · 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

Citations36
Published2021
Admission routes1
Has abstractyes

Explore more

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