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Record W7133096318

Microwave-Driven Natural Gas Pyrolysis in a Fluidized Bed Reactor for Hydrogen and Production of Solid Carbon

2024· dissertation· W7133096318 on OpenAlexaff
George Saegh

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldChemistry
TopicMicrowave-Assisted Synthesis and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNatural gasPyrolysisHydrogenMethaneFuel gasCarbon fibersHydrogen productionRaw materialFluidized bed
DOInot available

Abstract

fetched live from OpenAlex

Hydrogen has gained a lot of attention in the energy industry in the hopes of achieving clean sources of energy. Microwave-driven methane pyrolysis is a new method proposed to produce hydrogen. Microwave energy is implemented due to the efficiency and electrification of the process. However, natural gas would be the feedstock used on a commercial scale which explains the motivation behind this thesis. This thesis investigates a natural gas pyrolysis process by microwave heating for clean hydrogen production, utilizing a representative sample of pipeline natural gas. Natural gas was injected in a fluidized bed reactor filled with heated carbon particles inside a microwave cavity. The results showed that the natural gas breaks down to mainly hydrogen and solid carbon particles with a >90% methane conversion rate and hydrogen yield. In addition, natural gas pyrolysis can no longer be called emission free but rather low-carbon due to the presence of carbon dioxide in the natural gas.

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
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.300
Teacher spread0.286 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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