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Record W2972836172 · doi:10.1021/acs.iecr.9b02201

Internal Gas–Liquid Separation in Industrial Ebullated Bed Hydroprocessors

2019· article· en· W2972836172 on OpenAlexafffund
Chris Lane, Arturo Macchi, Craig A. McKnight, Jason Wiens, Adam Donaldson

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2019
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsSyncrude (Canada)University of OttawaDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaSyncrude
KeywordsBubbleEntrainment (biomusicology)Fluidized bedScalingMaterials scienceSeparation (statistics)Process engineeringNuclear engineeringMechanicsFlow (mathematics)Environmental scienceComputational fluid dynamicsResidence time (fluid dynamics)Waste managementComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

Ebullated bed reactors used in heavy oil upgrading internally recycle up to 90% of net liquid flow to maintain fluidized conditions. Internal phase separators result in significant gas re-entrainment within the recycled liquid, leading to high gas holdup (40 to 60%), reduced processing capacity, and over-cracking of vapor products. Phase separation in two commercial designs is assessed using computational fluid dynamics and compared to pilot-scale results. Liquid short-circuiting within first-generation recycle pan designs was identified as a key factor, leading to a reduced separation efficiency of 1 to 2 mm bubbles compared to second-generation flow-through designs. Negligible performance improvements were observed for <1 mm bubbles, highlighting the need to exercise caution when scaling these designs to hydroprocessing conditions where submillimeter bubble sizes are predominant. A practical correlation for gas separation efficiency based on the liquid residence time is proposed and compared to numerical and experimental data.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.270
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0010.002
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 head0.041
GPT teacher head0.301
Teacher spread0.260 · 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

Citations7
Published2019
Admission routes2
Has abstractyes

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