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Record W2613525918 · doi:10.1002/cjce.22894

Pyrolysis and char oxidation characteristics of oil shales and coal in a thermogravimetric analyzer

2017· article· en· W2613525918 on OpenAlexvenueno aff
Dong Kyoo Park, Eunhye Song

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsCharThermogravimetric analysisOil shalePyrolysisCoalOxidizing agentChemical engineeringChemistryIsothermal processBituminous coalMineralogyOrganic chemistryWaste managementThermodynamics

Abstract

fetched live from OpenAlex

Abstract Non‐isothermal thermogravimetric analysis was carried out on two different oil shales and sub‐bituminous coal in order to evaluate effects of the fuel properties and compare the reaction characteristics. The samples and their chars after the devolatilization were heated up to 900 °C at a constant heating rate of 10 K/min under inert and oxidizing atmospheres, respectively. Experimental results exhibit distinct behaviour between the samples for both pyrolysis and char oxidation processes. Pyrolysis of oil shale occurs in two temperature regimes corresponding to its organic and mineral degradation, and mineral matter plays important roles in production of the residual char at the second pyrolysis stage and its reactivity to the subsequent char oxidation. Kinetic analysis was performed using the global one‐ and multi‐step models with n th order reaction mechanism. Organic devolatilization processes of coal and oil shale are analyzed by three‐ and two‐step models, respectively, however mineral degradation of oil shale and char oxidation are analyzed by a one‐step model. Finally, a reasonable fit to the experimental data can be achieved for all the samples and their chars at different atmospheres.

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 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 score0.027
Threshold uncertainty score0.322

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)0.0000.000

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 head0.007
GPT teacher head0.183
Teacher spread0.176 · 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.

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

Citations7
Published2017
Admission routes1
Has abstractyes

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