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Record W2332912310 · doi:10.2118/165424-ms

Synergistic Interactions Between Different Components on Blending

2013· article· en· W2332912310 on OpenAlexaff
Ishpinder Kailey, Jacqueline A. Behles, Catherine Blackwell

Bibliographic record

VenueSPE Heavy Oil Conference-Canada · 2013
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsBaker Hughes (Canada)
Fundersnot available
KeywordsArithmetic underflowYield (engineering)AsphaltChemical engineeringSolubilitySurface tensionMaterials scienceChemistryComposite materialOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract Chemical demulsification is the most widely used method for breaking water-in-diluted bitumen emulsions in oil sands processing. In this paper, the properties and the performance of six intermediates and three blends were investigated. The intermediates and blends were characterized by their relative solubility number (RSN). The results showed that the RSN is an additive property. The dehydration efficiency of the blends was higher than the individual components at the same dosage, indicating there were synergistic interactions among the components on blending. The performance of the demulsifiers was correlated to the interfacial tension (IFT), yield stress of underflow, and bitumen loss to tailings. The equilibrium IFT results did not illustrate any correlation with the performance of the demulsifiers. The yield stress of the underflow, which included settled solids, water, and a rag layer increased with increasing dosage of either intermediate or blend. In addition, the bitumen loss to underflow increased with increasing dosage of either intermediate or blend. The yield stress and bitumen loss to underflow reduced on blending the intermediates. The bitumen loss to underflow increased the size of the aggregates present in the underflow, increasing their immobility and constriction to flow, and eventually a 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score1.000

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)0.0050.000

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 head0.031
GPT teacher head0.240
Teacher spread0.209 · 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 designOther design
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

Citations0
Published2013
Admission routes1
Has abstractyes

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