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Internally Heated Screw Pyrolysis Reactor (IHSPR) heat transfer performance study

2018· article· en· W2795841440 on OpenAlexfundno aff
Siow Hwa Teo, Hangyu Gan, Ahmad Rizal Alias, Li‐Hua Gan

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
FundersInstitute of Health Services and Policy Research
KeywordsHeat transferPyrolysisMaterials scienceNuclear engineeringThermodynamicsChemical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

1.5 billion end-of-life tyres (ELT) were discarded globally each year and pyrolysis is considered the best solution to convert the ELT into valuable high energy-density products.Among all pyrolysis technologies, screw reactor is favourable.However, conventional screw reactor risks plugging issue due to its lacklustre heat transfer performance.An internally heated screw pyrolysis reactor (IHSPR) was developed by local renewable energy industry, which serves as the research subject for heat transfer performance study of this particular paper.Zeroload heating test (ZLHT) was first carried out to obtain the operational parameters of the reactor, followed by the one dimensional steady-state heat transfer analysis carried out using SolidWorks Flow Simulation 2016.Experiments with feed rate manipulations and pyrolysis products analyses were conducted last to conclude the study.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.200
Teacher spread0.188 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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