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Probing the Impact of Asphaltene Contamination on Kaolinite and Illite Clay Behaviors in Water and Organic Solvents: A Calorimetric Study

2016· article· en· W2469065014 on OpenAlexafffund
Amin Pourmohammadbagher, John M. Shaw

Bibliographic record

VenueEnergy & Fuels · 2016
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
FundersVirtual Materials GroupConocoPhillipsNatural Sciences and Engineering Research Council of CanadaTotalShellBP
KeywordsAsphalteneIlliteKaoliniteSorptionClay mineralsChemical engineeringChemistryEnvironmental chemistryAdsorptionDesorptionMineralogyOrganic chemistry

Abstract

fetched live from OpenAlex

A detailed understanding of the impacts of trace compounds and asphaltene adsorption on the behavior of clays contributes to the development of production processes for heavy oils and bitumen with lower environmental impacts, to the treatment of tailings from mined bitumen, and to the mitigation of impacts from oil spills in natural environments. Probes, such as solution calorimetry, are sensitive to species transfer to and from clay surfaces and outcomes can be interpreted unambiguously when supplemented with thermogravimetric analysis and scanning electron microscopy measurements. In this study, the effects of asphaltene coating on the enthalpy of solution of kaolinite and illite clays in toluene, n -heptane, and deionized water were investigated at 60 °C and atmospheric pressure. Asphaltene coating increases organic compound sorption but does not impact water sorption vis-à-vis uncoated clay particles or water displacement from clay particles by organic liquids as solvents or as trace contaminants in water. Experimental outcomes are interpreted using a quantitative mass and energy balance model framework that isolates terms for solvent and trace contaminant sorption/desorption and surface energy effects. Mechanistic and quantitative insights underlying the stability of asphaltene-coated clay dispersions in tailings ponds and the behaviors of these clays in diverse industrial and natural environments are discussed.

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 score0.062
Threshold uncertainty score0.244

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 head0.011
GPT teacher head0.258
Teacher spread0.248 · 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

Citations20
Published2016
Admission routes2
Has abstractyes

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