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Record W4313297266 · doi:10.53907/enpesj.v2i2.115

Production of hydrogen and carbon nanofilaments using a novel reactor configuration: hydrodynamic study and experimental results

2022· article· en· W4313297266 on OpenAlexaff
Abir Azara, Jasmin Blanchard, Faroudja Mohellebi, EL Hadi Benyoussef, F. Gitzhofer, Nicolas Abatzoglou

Bibliographic record

VenueENP Engineering Science Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPyrolysisCarbon fibersFluidized bedOperabilityHydrogenMaterials scienceEthyleneHydrogen productionChemical engineeringCatalysisProcess engineeringEnvironmental scienceWaste managementComputer scienceChemistryOrganic chemistryComposite materialEngineeringComposite number

Abstract

fetched live from OpenAlex

A novel reactor configuration combining two beds, a central fluidized bed and an annular mobile bed, was designed for the production of hydrogen and carbon nanofilaments via dry reforming of gases produced from the pyrolysis of plastic waste. This combination allows for easy recovery of these nanomaterials and, since the mixture of catalyst and carbon formed is continuously fluidized, it also prevents blockage. Understanding the hydrodynamics is crucial for choosing the optimal operating conditions. Thus, a cold mock-up unit of the same size has been built and used. Since the gases produced by plastic pyrolysis are mainly composed of unsaturated hydrocarbons, the prototype reactor setup has been operated using ethylene as a surrogate molecule. The preliminary experimental results of the reactor operation with ethylene obtained so far are very promising and confirm the operability of the process. Next step is to operate continuously for longer time and reach a production of 1kg/h of carbon nanofilaments.

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)0.0010.000
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)0.0010.000

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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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