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Record W2055602767 · doi:10.1021/ef060370u

Kinetic Model for the Combustion of Coke Derived at Different Coking Temperatures

2006· article· en· W2055602767 on OpenAlexaff
Yan Jiao Ren, Nader Mahinpey, N. P. Freitag

Bibliographic record

VenueEnergy & Fuels · 2006
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsSaskatchewan Research Council (Canada)University of Regina
Fundersnot available
KeywordsCokeAsphalteneThermogravimetric analysisCombustionPetroleum cokeChemistryArrhenius equationReactivity (psychology)ThermodynamicsCoke strength after reactionOrganic chemistryActivation energy

Abstract

fetched live from OpenAlex

The oxidation behavior of coke samples derived from Neilburg oil and its asphaltenes at different coking temperatures was examined using thermogravimetric analysis (TGA). A two-coke model was proposed to describe the coke combustion processes. The two-coke model provided a better prediction than the conventional one-coke model, i.e., the classical Arrhenius model, as it matched the experimental data more accurately, especially at lower combustion temperatures. The two coke pseudocomponents might reasonably represent the cokes derived at different coking temperatures, and the two-coke model appeared potentially useful to interpret the coke combustion process and applicable to simulation. Our experimental data suggest that the coke's reactivity was inversely proportional to the coking temperature. With respect to the coke sources, the coke derived from whole oil showed higher reactivity than the one from asphaltenes, although the difference was modest.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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 designSimulation or modeling
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

Citations41
Published2006
Admission routes1
Has abstractyes

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