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Record W2331106815 · doi:10.1021/ie3028137

Influence of Structural Variations of Demulsifiers on their Performance

2012· article· en· W2331106815 on OpenAlexafffund
Ishpinder Kailey, Xianhua Feng

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2012
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsBaker Hughes (Canada)
FundersAlberta Innovates - Technology Futures
KeywordsArithmetic underflowEthylene oxideChemistryDemulsifierDehydrationSolubilityChemical engineeringYield (engineering)Surface tensionChromatographyMaterials scienceEmulsionCopolymerOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Chemical demulsification is the most widely used method for breaking water-in-diluted bitumen emulsions in oil sands processing. In this work, the properties and performance of six samples of ethylene oxide (EO)—propylene oxide (PO) block copolymer demulsifiers from two families were investigated. The demulsifiers were characterized by their relative solubility number (RSN), EO content, PO content, and molecular weight (MW). The results showed that the performance of the demulsifiers is correlated to the starting base compound, EO content, PO content, RSN, MW, degree of cross-linking, interfacial tension (IFT), yield stress of underflow, and bitumen loss. Demulsifiers with higher MW and more EO–PO branching had higher dehydration efficiencies when the EO content was varied from 0% to 40% at constant PO content. An increase in MW by cross-linking EO–PO copolymers improved the dehydration efficiency. In this work, an appropriate rheological method was developed to correlate the properties of the demulsifiers with the properties of the underflow. The yield stress of the underflow, including settled solids, water, and the rag layer, increased with increasing RSN value and dosage of demulsifier. At high dosages, the yield stress values were high because of an increased number of aggregates, which, in turn, restricted underflow. An increase in the RSN value of the demulsifiers led to more bitumen loss to the underflow, which increased the size of the aggregates present in the underflow, resulting in increased immobility and constriction and higher yield stress.

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)0.001
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 score0.041
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.061
GPT teacher head0.313
Teacher spread0.252 · 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

Citations50
Published2012
Admission routes2
Has abstractyes

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