MétaCan
Menu
Back to cohort
Record W1998971445 · doi:10.1002/cjce.20242

Coke yield and heat transfer in reaction of liquid–solid agglomerates of Athabasca vacuum residue

2010· article· en· W1998971445 on OpenAlexafffundvenue
Mohamed Ali Hassan Ali, Megan Courtney, Lisa Boddez, Murray R. Gray

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of Alberta
FundersSyncrude
KeywordsCokeAgglomerateYield (engineering)Residue (chemistry)ChemistryMaterials scienceAlloyMetallurgyChemical engineeringComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The ultimate coke yield was determined for agglomerates of Athabasca vacuum residue and coke particles by heating on Curie‐point alloy strips in an induction furnace at 503 and 530°C until all toluene‐soluble material was converted. Coke yield results from agglomerates were compared to the coke yield results from reacting thin films of vacuum residue. The average coke yield from the agglomerates was 23%, while the coke yield from thin films of 20 µm thickness was 11%, which supports the role of mass transfer in coke formation reactions. The ultimate coke yield was insensitive to vacuum residue concentration, agglomerate size, and reaction temperature. The temperature profile within agglomerates was measured by implanting a thermocouple, and a simple heat transfer model was used to describe the temperature variation with time. The effective thermal diffusivity of the agglomerates was 0.20 × 10 −6 m 2 /s. Control experiments on reaction in thin liquid films confirmed that heating rates in the range of 14.8–148 K/s had no effect on the ultimate yield of coke yield.

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 score0.001
Threshold uncertainty score0.384

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 head0.006
GPT teacher head0.179
Teacher spread0.174 · 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

Citations16
Published2010
Admission routes3
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicThermochemical Biomass Conversion ProcessesFrench-language works237,207