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Record W2327643856 · doi:10.1021/ef400314m

Suppression of Coke Formation during Bitumen Pyrolysis

2013· article· en· W2327643856 on OpenAlexafffund
Ashley Zachariah, Lin Wang, Shaofeng Yang, Vinay Prasad, Arno de Klerk

Bibliographic record

VenueEnergy & Fuels · 2013
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Resources CanadaHelmholtz-Alberta Initiative
KeywordsCokePyrolysisHydrogenChemistryChemical engineeringSolventYield (engineering)Organic chemistryMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Mild pyrolysis (400 °C) of bitumen was investigated to establish ways in which coke formation can be suppressed. Bitumen was diluted to various degrees with solvents that had different hydrogen transfer properties, namely, hydrogen donation, hydrogen shuttling, and poor hydrogen transfer properties. Additionally, the concentration of light products generated during bitumen pyrolysis was manipulated by pressure and batch/semibatch operation. Coke formation was suppressed by light material, whether added as a solvent or generated in situ during pyrolysis. As anticipated, hydrogen transfer was important, but coke formation was reduced by 35% at 10% concentration of even a poor hydrogen transfer solvent. Hydrogen availability and the H:C ratio of the reaction mixture were found to be particularly influential in determining whether coke formed. The results showed that light gases produced during pyrolysis were not irreversible reaction products, but continued to participate in the reaction network to moderate the pyrolysis process and suppress coke formation. Applied to industrial operation, evidence was provided to indicate that liquid yield can be increased and coke formation can be suppressed during visbreaking by cofeeding light gases, typically C 4 and lighter hydrocarbons.

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 categoriesInsufficient payload (model declined to judge)
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 score0.012
Threshold uncertainty score0.999

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)0.0020.000

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 head0.205
Teacher spread0.199 · 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.

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

Citations57
Published2013
Admission routes2
Has abstractyes

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