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Record W3141502700 · doi:10.1002/cjce.24289

Suppression of liquid slugs and phase separation through pipeline bends

2021· article· en· W3141502700 on OpenAlexafffundvenue
Loveday C. Igbokwe, G.F. Naterer, Sohrab Zendehboudi, Simon Pedersen, Stefan Jespersen

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPipeline (software)Separation (statistics)ChromatographyPhase (matter)Materials scienceChemistryGeologyEngineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

Abstract This study examines the suppression of liquid slugs in the transport and separation of multiphase flows in pipelines. Two well‐known slug control approaches are evaluated in this paper. The methods are employed to control and stabilize an undesired and unstable flow regime, optimize flow production, reduce operating costs, and in general, improve overall safety requirements of oil and gas pipelines. Unlike designs with an additional flowline to separate gas upstream, this study shows that active topside choking can suppress slugs and stabilize the system flowrates and pressures without the requirement of separation upstream of the topside valve. Careful choking is required to minimize production losses that can result from excess back pressure. A riser‐based, gas‐lift method reduces system instability and increases production. This study also reveals that negligible improvement in stability is achieved when large volumes of gas are injected. The system shifts into an annular flow regime when the injection is further increased. A large separator may be required to accommodate high gas volumes. This study shows that gas‐lift not coordinated with choking is not effective for slug mitigation through pipeline bends. This paper also presents and discusses new non‐dimensional correlations, including slug control inputs in the pipelines such as choke openings, based on new 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)0.000
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 score0.177
Threshold uncertainty score0.286

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 head0.009
GPT teacher head0.228
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

Citations4
Published2021
Admission routes3
Has abstractyes

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