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Record W4386445674 · doi:10.1002/9781394200306.ch14

Blue and Green Hydrogen Production, Distribution, and Supply for the Glass Industry and the Potential Impact of Hydrogen Fuel Blending in Glass Furnaces

2023· other· en· W4386445674 on OpenAlexaboutno aff
Michael J. Gallagher, Ashwin Vinod, Anne‐Cécile Roger

Bibliographic record

VenueCeramic transactions /Ceramic transactions · 2023
Typeother
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsnot available
Fundersnot available
KeywordsHydrogenHydrogen productionRenewable energyCombustorCombustionHydrogen fuelNatural gasProduction (economics)Environmental scienceWaste managementFuel cellsMaterials scienceProcess engineeringChemical engineeringEngineeringChemistry

Abstract

fetched live from OpenAlex

The global movement towards decarbonization spans across all industries, including Glass manufacturing. Hydrogen is expected to play a key role in decarbonizing industry. To make it easy to differentiate between the different hydrogen production methods/carbon intensities, industry has adopted the practice of referring to hydrogen using various colors. The most used colors are grey, blue, and green. Blue hydrogen is made from a hydrocarbon source. Green hydrogen is produced from a renewable energy source. Air Products has made significant investments in blue and green hydrogen and ammonia production. Currently, there are three major projects in the Middle East, Canada, and the United States in various stages of completion. The chapter also discusses some aspects of these projects including production, distribution, and supply methods. In addition, results using hydrogen as a fuel to replace natural gas combustion with existing oxy-fuel burner technology are presented.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.040

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.002

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 head0.010
GPT teacher head0.255
Teacher spread0.245 · 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
Published2023
Admission routes1
Has abstractyes

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