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Record W1910509689 · doi:10.1002/cjce.22152

Prediction of azeotropic behaviour by the inversion of functions from the plane to the plane

2014· article· en· W1910509689 on OpenAlexvenueno aff
Aline Lima Guedes, Francisco Duarte Moura Neto, Gustavo Mendes Platt

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAzeotropeNonlinear systemAlgebraic numberThermodynamicsPlane (geometry)MathematicsStatistical physicsMathematical analysisChemistryPhysicsGeometryQuantum mechanics

Abstract

fetched live from OpenAlex

Azeotropy is a thermodynamic phenomenon where liquid and vapour coexisting phases have the same composition. In binary mixtures, the azeotropy calculation is represented by a 2 × 2 nonlinear system of algebraic equations with temperature (or pressure) and one molar fraction as unknowns. On rare occasions, this nonlinear system exhibits two solutions, characterizing a double azeotrope. In this work, we calculate double azeotropes with a geometry‐based methodology: the numerical inversion of functions from the plane to the plane. We present results for two mixtures where the double azeotropy phenomenon occurs: the system benzene + hexafluorobenzene and the system 1,1,1,2,3,4,4,5,5,5‐decafluoropentane + oxolane. The persistence of double azeotropes across different pressures is made clear by this global geometric approach. Moreover the vanishing of the existence of a pair of azeotropes is explained by the coalescence of them in just one, as can be easily understood from a global geometric viewpoint presented of the nonlinear function involved. The results indicate that this methodology can be a powerful tool for a better understanding of nonlinear algebraic systems in phase coexistence problems.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.223

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 head0.006
GPT teacher head0.152
Teacher spread0.146 · 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 designSimulation or modeling
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
Published2014
Admission routes1
Has abstractyes

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