MétaCan
Menu
Back to cohort
Record W2315107983 · doi:10.1021/ef500489y

Deep-Vacuum Fractionation of Heavy Oil and Bitumen, Part I: Apparatus and Standardized Procedure

2014· article· en· W2315107983 on OpenAlexafffundabout
Orlando Castellanos Diaz, M. C. Sánchez-Lemus, F. F. Schoeggl, Marco A. Satyro, Shawn D. Taylor, Harvey W. Yarranton

Bibliographic record

VenueEnergy & Fuels · 2014
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsSchlumberger (Canada)Virtual Materials Group (Canada)University of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVacuum distillationDistillationBoiling pointContinuous distillationBoilingChemistryFraction (chemistry)FractionationAsphaltBatch distillationChromatographyRefineryFractional distillationMaterials scienceOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Distillation assays provide the most widely used characterization data for modeling crude oils in refinery processes but can only define a relatively small fraction of the boiling curve for heavy oils. One way to extend the distillation range for heavy oils is to lower the distillation pressure while still avoiding thermal cracking temperatures. A vacuum distillation apparatus (DVFA-I) that was previously used to measure the vapor pressure of heavy components was modified to fractionate heavy oils and bitumens. The modified apparatus (DVFA-II) is a batch distillation system without reflux operating at pressures down to approximately 0.01 Pa, compared with 100 Pa for a conventional vacuum distillation. With the standard procedure developed for DVFA-II, up to 50 wt % of a Western Canadian bitumen was distilled into eight cuts plus a residue. This is an improvement over the 26 wt % distilled by a spinning band vacuum apparatus. The cumulative weight percent distilled was repeatable to within 2.3% for a given boiling point range. The densities and molecular weights of the cuts from two fractionations increased monotonically versus the weight percent distilled and were repeatable to within 0.18 and 4.0%, respectively.

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 score0.320
Threshold uncertainty score0.452

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 head0.225
Teacher spread0.219 · 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

Citations27
Published2014
Admission routes3
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicPetroleum Processing and AnalysisFrench-language works237,207