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Record W1973689998 · doi:10.1002/cjce.21842

Characterising rag‐forming solids

2013· article· en· W1973689998 on OpenAlexaffvenue
Morvarid Madjlessi Kupai, Fan Yang, David Harbottle, Kevin Moran, Jacob H. Masliyah, Zhenghe Xu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSideritePyriteLayer (electronics)Naphthenic acidSurface layerChemistryChemical engineeringTotal dissolved solidsOrganic matterMaterials scienceMineralogyOrganic chemistryEnvironmental engineeringEnvironmental science

Abstract

fetched live from OpenAlex

Abstract In oil sands froth treatment, an undesirable intermediate layer, often accumulates during the separation of water–oil emulsions. The layer referred to as rag layer is a complex mixture of water, oil, solids and interfacially active components. The presence of a rag layer has a detrimental impact on the separation of water and fine solids from diluted bitumen. The current study focuses on characterisation of solids from a rag layer forming stream of a naphthenic froth treatment plant in an attempt to understand the mechanism of rag layer formation. Through detailed characterisation of rag‐forming and non‐rag‐forming solids, the mineralogy of solids and their contamination were shown to be critical to rag layer formation. The iron‐based minerals such as siderite and pyrite were found to be enriched within the rag layer. Analysis of surface organic complexes confirms a high level of organic matter associated with these solids through the binding of carboxylic acid group with iron on solids, resulting in a surface hydrophobicity susceptible for rag layer formation.

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 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 score0.018
Threshold uncertainty score0.475

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)0.0000.000

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 head0.005
GPT teacher head0.172
Teacher spread0.168 · 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

Citations21
Published2013
Admission routes2
Has abstractyes

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