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Record W2092377918 · doi:10.1021/ie020789h

Measurement of Adhesive Forces during Coking of Athabasca Vacuum Residue

2003· article· en· W2092377918 on OpenAlexafffund
Murray R. Gray, Zisheng Zhang, William C. McCaffrey, Iftikhar Huq, Lisa Boddez, Zhenghe Xu, Janet A.W. Elliott

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2003
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsSyncrude (Canada)University of Alberta
FundersCanada Research Chairs
KeywordsResidue (chemistry)AdhesiveMaterials scienceThermalChemistryChemical engineeringComposite materialThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Processes for thermal conversion of vacuum residues in fluidized or moving beds of solid particles require excellent distribution of feed liquid onto the solid particles and free flow of the solids. Forces between the solid particles will be determined by the changing quantity and properties of the liquid material during reaction. This paper presents a unique apparatus for measuring pull-off forces between particles due to reacting liquid films. In the case of 25−30-μm-thick films of Athabasca vacuum residue at 503 °C, the maximum pull-off force occurred after ca. 12 s of reaction and then declined gradually until no force was detected after ca. 24 s of reaction. These data suggest that solid particles coated with vacuum residue would be adhesive for this period of time, while the film of feed material is still liquid and undergoing reaction.

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 score0.005
Threshold uncertainty score0.009

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.271
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 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

Citations4
Published2003
Admission routes2
Has abstractyes

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