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Record W2058316530 · doi:10.1021/ie0206711

Mechanistic Study of the Carbothermal Reduction of Sulfur Dioxide with Oil Sand Fluid Coke

2003· article· en· W2058316530 on OpenAlexafffund
Cesar Bejarano, Charles Q. Jia, Keng H. Chung

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2003
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsSyncrude (Canada)University of Toronto
FundersSyncrudeGovernment of Ontario
KeywordsSulfurCokeCarbothermic reactionPetroleum cokeChemistrySulfur dioxideOil sandsChemical engineeringInorganic chemistryMaterials scienceOrganic chemistryAsphalt

Abstract

fetched live from OpenAlex

Carbothermal reduction is a reducing reaction involving carbonaceous materials at high temperatures. Oil sand fluid coke is a high-carbon byproduct of the thermal cracking of oil sand bitumen via a process called fluid coking. To lay a foundation for the development of a process that removes and converts sulfur dioxide into elemental sulfur, the kinetics of the carbothermal reduction of SO 2 by coke at 700−950 °C was investigated using a packed-bed reactor. Analysis using the shrinking core model revealed that the overall process is controlled jointly by surface chemical reaction and diffusion in a product ash layer. The existence of the layer was confirmed by SEM examination of a cross section of spent coke particles. The activation energy of the overall reaction was found to be 154 kJ/mol, which is in a good agreement with literature values. The sulfur balance was analyzed with data obtained using a total sulfur analyzer and a gas chromatograph. SEM-EDS analysis indicated that the ash layer was low in sulfur. At the ash−coke interface, however, an accumulation of sulfur was found that was attributed to C−S complexes. The chemical states of sulfur in the spent coke were determined using an X-ray photoelectron spectrometer. The sulfur in the raw coke was likely dominated by its thiophenic forms, whereas the sulfur in the ash layer was likely sulfite.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.283
Teacher spread0.239 · 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

Citations30
Published2003
Admission routes2
Has abstractyes

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