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Record W1983477798 · doi:10.1002/cjce.22068

Methanol production by bi‐reforming

2014· article· en· W1983477798 on OpenAlexvenueno aff
B.A.V. Santos, José M. Loureiro, Ana M. Ribeiro, Alı́rio E. Rodrigues, Adelino F. Cunha

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsnot available
Fundersnot available
KeywordsSteam reformingFossil fuelCarbon dioxideGreenhouse gasNatural gasMethanolMethaneCarbon dioxide reformingCarbon-neutral fuelWaste managementEnvironmental scienceBiomass (ecology)SyngasChemistryEcologyHydrogen productionEngineeringCatalysisOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Abstract Population growth and emerging economies have as consequence increasing energy demands associated with fossil fuel depletion and environmental impacts. A new philosophy emerges: the concept of green chemistry. Carbon dioxide, a well‐known greenhouse gas, is a source for the production of fine chemicals and fuels such as methanol. It appears in abundance due to anthropogenic human activities. Nowadays, methanol is typically produced from synthesis‐gas which requires conventional fossil fuels; however, the availability of these fuels is limited. As an alternative, the vent streams of steam reforming units, which are rich in carbon dioxide and steam, can be used together with methane (natural gas) in a bi‐reforming process for methanol synthesis. The essential idea is that carbon dioxide and water are recycled like in a synthetic photosynthesis process.

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 score0.005
Threshold uncertainty score0.017

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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

Citations41
Published2014
Admission routes1
Has abstractyes

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