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Record W3170140326 · doi:10.1016/j.cis.2021.102455

Emulsions in external electric fields

2021· review· en· W3170140326 on OpenAlexafffund
Johan Sjöblom, Sameer Mhatre, Sébastien Simon, R. Skartlien, Geir Humborstad Sørland

Bibliographic record

VenueAdvances in Colloid and Interface Science · 2021
Typereview
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsUniversity of Calgary
FundersNorges ForskningsrådUniversity of AlbertaNorges Teknisk-Naturvitenskapelige UniversitetAker Solutions ASA
KeywordsElectric fieldDemulsifierDrop (telecommunication)ElectrohydrodynamicsCoalescence (physics)BreakupOil dropletPulmonary surfactantChemical physicsChemistryNanotechnologyMaterials scienceAsphalteneChemical engineeringEmulsionMechanicsOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Water is co-produced with crude oils, generally in the form of water-in-crude oil emulsions. The oil and water phases need to be separated before export. Separation is performed in gravity separators with the addition of chemical demulsifiers and, sometimes, with the application of an electric field by using an electrocoalescer. The present article reviews several aspects of electrocoalescence by considering the effect of the electric field from the molecular to a macroscopic scale: the oil-water interface, single drop effects, two drop interactions, and finally emulsions at laboratory scales. Experimental results together with Dissipative Particle Dynamics (DPD) simulation results are presented. The review begins with water-oil interface under an electric field and followed by single drop electrohydrodynamics. The electric field is shown to influence the adsorption of crude oil indigenous surface-active components (asphaltenes) due to the electrohydrodynamic (EHD) flows. The interactions between two droplets in the presence of electric field and the factors governing the drop-drop coalescence are discussed in detail. DPD simulations help to elucidate thin film breakup during (electro)-coalescence of two water droplets, where the oil film has drained out to nanometer thickness. The film is comprised of surfactant and demulsifier molecules, and the simulations capture the pores formation in the film when a DC field is applied. The results demonstrate influence of the molecular structure of the surfactant and demulsifier, and their interactions. The subsequent section describes experimental techniques to assess the resolution of crude oil emulsions at the laboratory scale. The focus is on low-field Nuclear Magnetic Resonance (LF-NMR) which allows a determination of various emulsion features such as the droplet size distribution (DSD) and the brine profile (variation of the concentration of water with the height of the emulsion sample) and their evolution with time. Application of the technique in emulsion treatment involving chemical demulsifiers and electric field is presented. The review concludes with description of commercial industrial electrocoalecers such as the Vessel Internal Electrostatic Coalescer (VIEC) and the Compact Electrostatic Coalescer (CEC).

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.303
Teacher spread0.295 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations69
Published2021
Admission routes2
Has abstractyes

Explore more

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