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Record W6884454330 · doi:10.1021/ie401918e.s001

Kinetic\nStudies of a Novel CO<sub>2</sub> Gasification\nMethod Using Coal from Deep Unmineable Seams

2016· article· en· W6884454330 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2016
Typearticle
Languageen
FieldEngineering
TopicCoal Properties and Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsCoalCarbon fibersCharCore (optical fiber)Coal miningInert

Abstract

fetched live from OpenAlex

Seven\ncoal samples taken from cores drilled in the Cretaceous Mannville\nGroup were used for investigation of coal properties and carbon dioxide\n(CO<sub>2</sub>) gasification. The depths of the cores ranged between\n700 and 800 m below the surface in the Western Canadian Sedimentary\nBasin. A new method was developed with an average heating rate of\n200 K/min using CO<sub>2</sub> as the gasifying agent from the experiment’s\nbeginning until its end. The coal properties of the seven coals from\nthese deep coal seams showed certain similarities and variations.\nThere is an obvious relationship between the reactivity and the material\nproperties determined in the study. In particular, the specific surface\narea calculated relative to the carbon content measured in the ultimate\nanalysis showed a correlation with the reactivity. The ash content\nand composition also appeared to influence char reactivity. The gasification\nbehaviors of the in situ coals were compared to those of two surface-mined\ncoals. The new method of coal gasification showed a significant difference\nto those that were heated up in an inert gas, such as nitrogen, to\nthe target temperature. A maximum rate of reaction did not exist when\nthe new method was used, and the integrated core model gave better\nresults than the commonly used random pore model in terms of kinetic\nmodeling.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

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)0.0080.000

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 head0.076
GPT teacher head0.257
Teacher spread0.182 · 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 teacher head, not a consensus.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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