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Record W3088510365 · doi:10.1021/acs.iecr.0c03454

Fluid Dynamics Modeling of a Commercial Ebullated Bed Hydroprocessor

2020· article· en· W3088510365 on OpenAlexafffund
Jacob Mach, Adam Donaldson, Jan B. Haelssig, Jason Wiens, John Adjaye, Arturo Macchi

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2020
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsSyncrude (Canada)Dalhousie UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaSyncrude
KeywordsDistributorBubbleMechanicsDragFlow (mathematics)Liquid flowVolumetric flow rateFluid dynamicsTwo-fluid modelMultiphase flowEnvironmental scienceMaterials sciencePetroleum engineeringThermodynamicsPhysicsGeology

Abstract

fetched live from OpenAlex

A new multiphase fluid dynamics model for a commercial ebullated bed hydroprocessor was developed. The impact of the gas–liquid distribution system is now explicitly included through new submodels for bubble size distribution and drag coefficients. The size distribution submodel is coupled with the existing gas–liquid separation submodel to better predict recycled gas and liquid flow rates. Either the mass of the catalyst inventory or recycle pump curve can be specified as inputs to converge the model; the former is not always well known during operation in which case the latter can be used after making a few assumptions. A sensitivity analysis was performed to study the impact of fresh treat gas velocity, catalyst mass, phase properties, and reactor internals on recycled gas and liquid flow rates, bubble size distribution, and bed liquid holdup. A 0.2 mm shift in bubble size distribution toward larger sizes was found to significantly increase bed liquid holdup, suggesting that distributor modification/redesign could help improve the capacity of the hydroprocessor.

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 categoriesMeta-epidemiology (narrow)
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 score0.298
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.109
GPT teacher head0.296
Teacher spread0.187 · 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 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

Citations6
Published2020
Admission routes2
Has abstractyes

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