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Record W1993712045 · doi:10.1021/ie901031n

Prediction of Density and Viscosity of Bitumen Using the Peng−Robinson Equation of State

2009· article· en· W1993712045 on OpenAlexaff
Herbert Loría, Pedro Pereira‐Almao, Marco A. Satyro

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2009
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Calgary
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsViscosityEquation of stateThermodynamicsAsphaltStatistical physicsChemistryMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Density and viscosity are important quantities required in engineering design for production, fluid transportation, and processing. However, there is no satisfactory theory for the calculation of these properties for bitumen. The principal objective of this paper is to obtain thermodynamic models to predict the density and viscosity of bitumen on the basis of the translated version of the Peng−Robinson equation of state. In the density calculation, a consistent correction to improve the liquid-phase volume estimation was applied. The density model evidenced a small percent average absolute error regarding experimental data (less than 1%). The model for viscosity was based on a modification of the Enskog’s equation. This modification allowed the prediction of bitumen viscosity using an equation of state along with a substance- and temperature-dependent parameter; this approach showed good accuracy with respect to experimental data. An important advantage of these models is the possibility of estimating viscosities at different pressures.

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)0.001
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 score0.246
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.107
GPT teacher head0.300
Teacher spread0.193 · 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

Citations18
Published2009
Admission routes1
Has abstractyes

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