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Record W3163972458 · doi:10.1021/acs.iecr.1c01176

Wax Recovery from the Pyrolysis of Virgin and Waste Plastics

2021· article· en· W3163972458 on OpenAlexaff
S.M. Al–Salem, Animesh Dutta

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsUniversity of Guelph
FundersKuwait Foundation for the Advancement of Sciences
KeywordsWaxLow-density polyethyleneHigh-density polyethylenePolyethyleneDiesel fuelMaterials sciencePolyolefinPyrolysisCarnauba waxHeat of combustionFlash pointParaffin waxRaw materialPulp and paper industryWaste managementComposite materialChemistryOrganic chemistryCombustion

Abstract

fetched live from OpenAlex

Thermochemical conversion is an effective technique for the treatment of polyolefin plastics to produce value-added products, including oils and chemical waxes. The recovery of wax from the pyrolysis of virgin high-density polyethylene (HDPE), low-density polyethylene (LDPE), and plastic solid waste (PSW) in a patented fixed-bed reactor has been investigated in this study. The highest wax yield (64.5 wt %) was obtained from LDPE at 500 °C. Results show that the average densities of the waxes recovered from PSW, HDPE, and LDPE are 851.7 ± 1.0, 849.4 ± 5.3, and 879.5 ± 2.2 kg m –3, respectively. These values are similar to that of commercial wax and slightly higher than that of commercial paraffin wax. Considering their energy content, the waxes studied can be used as sources of fuel. The calorific values of the recovered waxes are estimated to be in the range of 45.61–46.22 kJ g –1, which is in the acceptable range of commercial kerosene, gas oil, and light fuel oil. Furthermore, the waxes obtained have flash points that are above the specifications of diesel fuel, indicating that they fall within the acceptable flammability range.

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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.278
Teacher spread0.229 · 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

Citations57
Published2021
Admission routes1
Has abstractyes

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