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Record W2326393373 · doi:10.1021/ie401918e

Kinetic Studies of a Novel CO<sub>2</sub> Gasification Method Using Coal from Deep Unmineable Seams

2013· article· en· W2326393373 on OpenAlexaffabout
Rico Silbermann, Arturo Gomez, Ian D. Gates, Nader Mahinpey

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2013
Typearticle
Languageen
FieldEngineering
TopicCoal Properties and Utilization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCoalCharReactivity (psychology)Carbon fibersCarbon dioxideCoal miningMineralogyChemistryNitrogenInertInert gasCarbochemistryGas compositionGeologyChemical engineeringEnvironmental scienceMaterials scienceOrganic chemistryThermodynamicsComposite numberComposite materialCoal liquefaction

Abstract

fetched live from OpenAlex

Seven coal samples taken from cores drilled in the Cretaceous Mannville Group were used for investigation of coal properties and carbon dioxide (CO 2 ) gasification. The depths of the cores ranged between 700 and 800 m below the surface in the Western Canadian Sedimentary Basin. A new method was developed with an average heating rate of 200 K/min using CO 2 as the gasifying agent from the experiment’s beginning until its end. The coal properties of the seven coals from these deep coal seams showed certain similarities and variations. There is an obvious relationship between the reactivity and the material properties determined in the study. In particular, the specific surface area calculated relative to the carbon content measured in the ultimate analysis showed a correlation with the reactivity. The ash content and composition also appeared to influence char reactivity. The gasification behaviors of the in situ coals were compared to those of two surface-mined coals. The new method of coal gasification showed a significant difference to those that were heated up in an inert gas, such as nitrogen, to the target temperature. A maximum rate of reaction did not exist when the new method was used, and the integrated core model gave better results than the commonly used random pore model in terms of kinetic modeling.

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 score0.008
Threshold uncertainty score0.015

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)0.0000.000

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 head0.215
GPT teacher head0.363
Teacher spread0.148 · 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

Citations32
Published2013
Admission routes2
Has abstractyes

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