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Record W2315072662 · doi:10.1021/ef2014259

Novel Magnetic Demulsifier for Water Removal from Diluted Bitumen Emulsion

2011· article· en· W2315072662 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnergy & Fuels · 2011
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDemulsifierEmulsionMagnetic nanoparticlesMagnetic separationChemical engineeringOil dropletAqueous solutionNanoparticleAqueous two-phase systemMaterials scienceSolventAsphalteneChemistryChromatographyNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

The production of conventional crude oil and bitumen often faces the challenges in removing residual water from stable water-in-oil emulsions. The chemical demulsifier is commonly employed to enhance water removal because of its high efficiency and simplicity in operation. In this study, a novel magnetic demulsifier with a surface-active ethyl cellulose (EC) grafted on magnetic nanoparticle surfaces, called M-EC, was investigated for water removal from water-in-diluted bitumen emulsions. The M-EC was demonstrated to be interfacially active and magnetically responsive. The interfacial activity of EC on the surface of novel M-EC nanoparticles allowed them to be effectively attached to otherwise stable emulsified water droplets in diluted bitumen emulsions. The M-EC tagged water droplets were readily removed by an external magnetic field. When a simple magnetic separation was combined with tagging of emulsified water droplets by M-EC nanoparticles, our experimental results showed a more than 90% removal of the original water from the diluted bitumen. Such a combination led to a separation time about 10 times faster than corresponding demulsification by chemical EC. The external magnetic field was found to enhance the coalescence of magnetically tagged water droplets in emulsion, producing a much smaller volume of sludge and hence leading to a minimal hydrocarbon loss to waste aqueous phase. The chemical bonding of interfacially active EC on the surface of magnetic nanoparticles and the magnetic property of M-EC allowed the spent M-EC nanoparticles to be readily recovered by magnetic separation and regenerated by solvent washing. The regenerated M-EC was found to retain its interfacial activity and be effective in breaking the diluted bitumen emulsions after reuse for 10 cycles. Application of M-EC nanoparticles to an industrial bitumen froth showed a minimal water removal of greater than 80%, demonstrating their promising applications to industry demulsification. The current study demonstrated that magnetic demulsification with tailor-designed magnetic demulsifiers represents a new direction of removing emulsified water from heavy oil and diluted bitumen emulsions.

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.

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 score0.184
Threshold uncertainty score0.767

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)0.0010.000

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 head0.017
GPT teacher head0.203
Teacher spread0.186 · 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