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Record W1984028124 · doi:10.1002/aic.10581

Prediction of global VLE for mixtures with improved renormalization group theory

2005· article· en· W1984028124 on OpenAlexaff
Jianguo Mi, Chongli Zhong, Yigui Li, Yiping Tang

Bibliographic record

VenueAIChE Journal · 2005
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsHoneywell (Canada)
Fundersnot available
KeywordsRenormalization groupContext (archaeology)ThermodynamicsStatistical physicsCritical point (mathematics)Phase diagramGroup (periodic table)Correlation function (quantum field theory)PhysicsPhase (matter)ChemistryMathematicsMathematical physicsMathematical analysisQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract The recently proposed renormalization group (RG) theory is reformulated within the context of density functional theory and applied to predicting global vapor–liquid equilibria (VLE) of Lennard–Jones chain pure fluids and fluid mixtures. An accurate equation based on the solution of the first‐order mean‐sphere approximation (FMSA) is adopted outside the critical region. Inside the critical region, the direct correlation function of FMSA is incorporated into the new RG transformation to describe the long‐range fluctuation, which is conformal to general inhomogeneous studies. The new RG theory is applied to correcting real mixture phase envelopes, as well as corresponding phase diagrams of pure compounds for the critical region. The calculated results are in substantial agreement with those from experiment and molecular simulation both inside and outside the critical region. The new method is highly predictive because no adjustable parameters and no mixing rule are needed for both model and real fluid mixtures. © 2005 American Institute of Chemical Engineers AIChE J, 2006

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: none
Teacher disagreement score0.807
Threshold uncertainty score0.230

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 head0.200
Teacher spread0.194 · 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

Citations24
Published2005
Admission routes1
Has abstractyes

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