MétaCan
Menu
Back to cohort
Record W2323379817 · doi:10.1021/ef200345g

Understanding Asphaltene Dispersants for Paraffinic Solvent-Based Bitumen Froth Treatment

2011· article· en· W2323379817 on OpenAlexaff
Xianhua Feng, Sanyi Wang

Bibliographic record

VenueEnergy & Fuels · 2011
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsBaker Hughes (Canada)
Fundersnot available
KeywordsDispersantAsphalteneArithmetic underflowChemical engineeringSolventChemistryMaterials scienceAsphaltChromatographyOrganic chemistryComposite materialDispersion (optics)

Abstract

fetched live from OpenAlex

Asphaltene dispersants are used in the paraffinic solvent-based bitumen froth treatment process to increase the fluidity of underflow that is made of precipitated asphaltene aggregates, water, and mineral solids. In this work, the yield stress of the underflow was used to define the fluidity of underflow and to evaluate the performance of asphaltene dispersants. The performance of asphaltene dispersants was also examined using microscope, tensiometer, and Dean–Stark composition analysis. Results show that asphaltene dispersants prevent asphaltene particles from forming large aggregates by modifying their surface properties. Application of an effective asphaltene dispersant not only results in much lower yield stress of the underflow, but also brings more mineral solids and asphaltenes from rag layer to the underflow.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 communication0.0000.001
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 head0.112
GPT teacher head0.270
Teacher spread0.158 · 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 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

Citations8
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicPetroleum Processing and AnalysisFrench-language works237,207