MétaCan
Menu
Back to cohort
Record W2320212829 · doi:10.1021/ef4010847

Copyrolysis of Oxygenate-Containing Materials with Bitumen

2013· article· en· W2320212829 on OpenAlexaff
Elaheh Toosi, William C. McCaffrey, Arno de Klerk

Bibliographic record

VenueEnergy & Fuels · 2013
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOxygenatePyrolysisLigninAsphaltCokeChemistryCelluloseYield (engineering)Chemical engineeringCoalPulp and paper industryOrganic chemistryWaste managementMaterials scienceCatalysisComposite material

Abstract

fetched live from OpenAlex

Thermal upgrading of bitumen at lower temperature can increase the overall liquid yield; however, from a practical point of view, decreasing the temperature leads to a lower reaction rate, eroding its industrial value. In order to increase pyrolysis rate at lower temperatures, copyrolysis of bitumen with potentially more reactive oxygenate-containing materials was proposed. The overall aim was to increase liquid yield and decrease gas yield and coke production, while increasing the pyrolysis rate. The oxygenate-containing materials that were investigated were partially oxidized bitumen, coals of different rank (lignite, subbituminous, and bituminous) and biomass-derived components (cellulose and lignin). No detectable increase in propagation rate, or decrease in the onset temperature of pyrolysis were observed due to addition of the oxygenate-containg materials. However, synergism with respect to a decrease in gas yield and organic residue was found during copyrolysis with some of the materials. There was meaningful synergism during copyrolysis with bituminous coal and lignin.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.005
GPT teacher head0.172
Teacher spread0.167 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicThermochemical Biomass Conversion ProcessesFrench-language works237,207