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Record W4406542199 · doi:10.1002/cjce.25602

Pyrolysis kinetics of the carbonization of high‐sulphur anthracite for activated carbon production

2025· article· en· W4406542199 on OpenAlexaffvenue
Chen Wang, Guofu Dai, Xuesen Chai, Chao Peng, Jinke Zhao, Weinan Wang, Zhijie Fu, Chenlong Duan

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicMining and Gasification Technologies
Canadian institutionsUniversity of British Columbia
FundersFundamental Research Funds for the Central Universities
KeywordsAnthraciteCarbonizationPyrolysisActivated carbonSulfurKineticsCarbon fibersChemical engineeringChemistryProduction (economics)Waste managementMaterials scienceOrganic chemistryCoalAdsorptionComposite materialEngineeringComposite numberEconomics

Abstract

fetched live from OpenAlex

Abstract The knowledge of pyrolysis kinetics of coal carbonization is essential for the optimization of reactor design and operation for activated carbon production. In the current work, non‐isothermal thermodynamics is used to study the pyrolysis process in the preparation of activated carbon from the high‐sulphur anthracite. It is seen that the co‐pyrolysis of multi‐component coal mixtures can significantly decrease the initial reaction Ea. With a heating rate of 5–20 K/min, the Ea is found to be 18–314 KJ/mol by the distributed activation energy model (DEAM). The similar Ea values of 18–314 KJ/mol and 25–344 KJ/mol are obtained by the integral isoconversional methods of Flynn‐Wall‐Ozawa (FWO) and Kissinger‐Akahira‐Sunose (KAS), respectively. With the increase of pyrolysis degree, the required activation energy increases accordingly. Based on the KAS model, the pyrolysis kinetic equations of high‐sulphur anthracite and its blended coal were derived using the master curve method.

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 score0.002
Threshold uncertainty score0.006

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)0.0020.000

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 head0.006
GPT teacher head0.178
Teacher spread0.172 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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