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Record W4226047711 · doi:10.1021/acs.iecr.1c03373

Pyrolysis of High-Density Polyethylene in a Fluidized Bed Reactor: Pyro-Wax and Gas Analysis

2021· article· en· W4226047711 on OpenAlexafffund
Shakirudeen A. Salaudeen, S.M. Al–Salem, Sonu Sharma, Animesh Dutta

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsUniversity of GuelphUniversity of Prince Edward Island
FundersNatural Sciences and Engineering Research Council of CanadaKuwait Institute for Scientific Research
KeywordsWaxHigh-density polyethylenePolyethylenePyrolysisCrystallinityMaterials scienceFluidized bedPyrolytic carbonEthyleneChemical engineeringHeat of combustionChemistryOrganic chemistryComposite materialCombustionCatalysis

Abstract

fetched live from OpenAlex

This study investigates the pyrolysis of high-density polyethylene (HDPE) in the fluidized bed reactor of a patented system. The experiments were performed at 500 °C, and the effect of olivine as a bed additive was studied. The HDPE material has a melting peak and crystallinity of 131 °C and 61.7%, respectively. Results revealed that wax is the dominant pyrolytic product. The addition of olivine in the fluidized bed increased the wax yield from 45.6 to 66 wt % and enhanced the formation of olefins. It was found that the pyro-wax has a high energy content (44.82 ± 0.24 MJ kg–1) and can be a potential source of fuel. The heating value is comparable to the energy content of conventional fuels. In addition, the results showed that chemicals in the pyrolytic product are dominated by aliphatic compounds. The pyro-wax has less branched alkyl chains than commercial waxes. The presence of many −C═C– groups in the pyro-wax indicates the formation of olefinic groups, and they are more than those in commercial waxes. Analysis of the pyro-gas revealed the dominance of ethylene, propylene, and hydrogen with yields of 8.68, 7.50, and 7.11 wt %, respectively, for the experiment conducted in the presence of olivine.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.013
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.289
Teacher spread0.251 · 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

Citations33
Published2021
Admission routes2
Has abstractyes

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