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Partial Upgrading of Bitumen: Impact of Solvent Deasphalting and Visbreaking Sequence

2017· article· en· W2746512643 on OpenAlexafffund
Ashley Zachariah, Arno de Klerk

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Resources CanadaShandong Academy of SciencesHelmholtz-Alberta InitiativeHelmholtz-GemeinschaftUniversity of Alberta
KeywordsAsphaltSolventChemistryYield (engineering)Chemical engineeringPulp and paper industryMaterials scienceOrganic chemistryMetallurgyComposite material

Abstract

fetched live from OpenAlex

Solvent deasphalting and visbreaking are two important technologies in the development of processes for partial upgrading of oilsands-derived bitumen to improve oil fluidity for pipeline transport. This work investigated the impact of the process sequence, solvent deasphalting followed by visbreaking (SDA-Vis) compared to visbreaking followed by solvent deasphalting (Vis-SDA). Thermal conversion during visbreaking was performed at 380 °C for 85 min, and solvent deasphalting was performed with n-pentane. Using this combination of processes in either sequence changed bitumen from a viscosity-limited fluid to a density-limited fluid with respect to pipeline specifications. The density and viscosity of the oil products from SDA-Vis and Vis-SDA were comparable. It was found that SDA-Vis achieved 2 wt % higher liquid yield than Vis-SDA. Conversely, Vis-SDA produced an oil product with higher hydrogen to carbon ratio compared to SDA-Vis. This difference could be explained in terms of hydrogen transfer during thermal conversion. Overall, the diluent requirements to meet pipeline fluidity requirements could be reduced by 40% relative to the Cold Lake bitumen feed.

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 score0.055
Threshold uncertainty score0.431

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 head0.024
GPT teacher head0.303
Teacher spread0.278 · 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

Citations56
Published2017
Admission routes2
Has abstractyes

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