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Formation of CH<sub>4</sub> during K<sub>2</sub>CO<sub>3</sub>-Catalyzed Steam Gasification of Ash-Free Coal: Influence of Catalyst Loading, H<sub>2</sub>O/H<sub>2</sub> Ratio, and Heating Protocol

2015· article· en· W2400540730 on OpenAlexafffund
Jan Kopyscinski, Charles A. Mims, Josephine M. Hill

Bibliographic record

VenueEnergy & Fuels · 2015
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of CalgaryUniversity of TorontoMcGill University
FundersCarbon Management CanadaUniversity of Alberta
KeywordsCatalysisMethaneChemistryCarbon monoxidePotassiumCoalCarbon fibersSyngasDrop (telecommunication)Wood gas generatorHydrogenChemical engineeringInorganic chemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Potassium-catalyzed steam gasification experiments of ash-free coal were conducted in a drop-down reactor to study the influence of the catalyst loading, temperature, H 2 O/H 2 ratio, and heating protocol. The higher the catalyst loading, the faster the carbon conversion; however, at an initial K/C ratio of 0.1, a saturation effect (decrease in reactivity per potassium atom) was observed. At higher gasification temperatures, this saturation effect was more pronounced and likely caused by increased potassium mobility. The catalyst accelerated predominately the oxygen transfer reactions and not the methane formation. Methane is produced via direct hydrogenation of the carbon surface. The selectivity to methane and carbon monoxide was increased by reducing the H 2 O/H 2 ratio, which would represent conditions in a gasifier away from the gas inlet. Lastly, the heating protocol influenced mainly the initial rate of gasification up to 20% carbon conversion; beyond that, the rates were similar.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.003
Open science0.0010.001
Research integrity0.0010.001
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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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; both teacher heads agree on what is shown here.

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

Citations11
Published2015
Admission routes2
Has abstractyes

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