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Kinetics of ethane exsolution and dissolution in bitumen

2024· article· en· W4404182463 on OpenAlexafffund
Shakerullah Turkman, Devjyoti Nath, Mahmood Abdi, Hassan Hassanzadeh

Bibliographic record

VenueInternational Journal of Heat and Mass Transfer · 2024
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDissolutionKineticsMaterials scienceAsphaltThermodynamicsChemical engineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

Dissolution and exsolution of gases are common in many engineering applications. Dissolution and exsolution occur across a gas-liquid interface when the equilibrium condition is disturbed. This study presents experimentally measured data on the exsolution and dissolution kinetics of ethane and bitumen (a viscous liquid) system across a temperature range of 80–140 °C and pressure differences of 0.69 and 0.35 MPa. Analytical models were adopted to estimate the exsolution and dissolution coefficients from the measured data for the ethane/bitumen system. The diffusivity values for the exsolution and dissolution processes were estimated to range from (2.65–10.48) × 10 −8 m 2 /s and (0.73–6.18) × 10 −9 m 2 /s, respectively, at a pressure difference of 0.69 MPa, and from (1.61–8.33) × 10 −8 m 2 /s and (0.89–10.78) × 10 −9 m 2 /s, respectively, at a pressure difference of 0.35 MPa. For both pressure differences, the exsolution kinetics were shown to be faster than dissolution in the ethane/bitumen system. This was also confirmed by higher activation energy for the exsolution process calculated using the Arrhenius equation. The results offer valuable insights into the kinetics of gas exsolution and dissolution, with applications in designing and optimizing processes where nonequilibrated gases and liquids are brought into contact.

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)0.001
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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.259
Teacher spread0.250 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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