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Prediction of the Distillate and SARA Content of Visbroken Bitumen, Vacuum Bottoms, and Deasphalted Oil

2024· article· en· W4402940715 on OpenAlexafffundabout
Jose Beleno, Amir Abbaspourmehdiabadi, F. F. Schoeggl, Harvey W. Yarranton

Bibliographic record

VenueEnergy & Fuels · 2024
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaConocoPhillips CanadaAlberta InnovatesSuncor Energy IncorporatedCanadian Natural Resources Limited
KeywordsAsphaltDistillationCoker unitVacuum distillationChemistryAsphaltenePulp and paper industryEnvironmental scienceChromatographyMaterials scienceOrganic chemistryEngineeringComposite material

Abstract

fetched live from OpenAlex

Methods to predict the final properties of visbroken bitumen and bitumen fractions are useful for the design of upgrading processes. Previously, correlations were proposed for the density, viscosity, and stability of visbroken products as a function of the measured feed properties, conversion, and product composition. The composition was defined in terms of the distillate content ( D ) and the saturate, aromatic, resin, and pentane-insoluble asphaltene contents of the distillation residue (SARA). However, for design purposes, it is necessary to predict rather than measure the product composition. The objective of this study was to develop correlations for the product distillates, saturates, aromatics, resins, and asphaltenes (DSARA) composition. To do so, seven different feeds including three Western Canadian bitumens, two partially deasphalted bitumens, a fully deasphalted bitumen, and a vacuum residue fraction were thermally cracked in two different continuous visbreakers at constant pressure over a range of conversions. The simulated distillation assay, gas yield, pentane-insoluble asphaltene content, toluene-insoluble (TI) content, distillate content, and residue SARA contents of each feed and product were measured. Conversions were determined from the SimDist assays and ranged from 5 to 46%. First, correlations were developed for the product DSARA contents as functions of the measured conversion and feed composition. The average absolute deviation (AAD) for the correlated composition was within the experimental error (±1.0 to ±3.0 wt % depending on the component). Next, the correlations were applied with a conversion determined from the reactor temperature profile and space time by using a previously developed method. Finally, the correlations were applied with feed DSARA content determined from readily available feed oil measurements (SimDist assay, oil density, and asphaltene content) using a previously developed method. In all cases, the AAD remained within experimental error.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.016
GPT teacher head0.212
Teacher spread0.196 · 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 designSimulation or modeling
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

Citations2
Published2024
Admission routes3
Has abstractyes

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