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Record W4391381424 · doi:10.1002/cben.202300049

Risk Identification and Safety Technology for Hydrogen Production from Natural Gas Reforming

2024· article· en· W4391381424 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemBioEng Reviews · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsUniversity of Calgary
FundersWuhan University of Science and TechnologyWuhan UniversityShuangchuang Program of Jiangsu ProvinceGovernment of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsProduction (economics)Natural gasIdentification (biology)Hydrogen productionEnvironmental scienceBusinessWaste managementHydrogenEngineeringChemistryEconomics

Abstract

fetched live from OpenAlex

Abstract The hydrogen production from natural gas has advantages of low investment, low carbon emission, and high hydrogen production rate. This paper briefly describes the technical overview of hydrogen production from natural gas reforming and identifies its risk factors. According to the dangerous characteristics of high reaction temperature, easy leakage of reaction medium, flammability, and explosion in the process, the intrinsic safety of the process is discussed in combination with relevant research and industrial experience. The safety requirements of key equipment and materials are introduced in detail, followed by the optimization methods of process safety that can be taken in the engineering process. Besides, the accident prevention measures for emergency shutdown and fire explosion are summarized. Finally, the future research demands are put forward from the perspective of research and development, which is instructive for the safe hydrogen production from natural gas in the future.

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.

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.363
Teacher spread0.313 · 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