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Record W2144623907 · doi:10.1002/aic.10292

Wax precipitation modeled with many mixed solid phases

2004· article· en· W2144623907 on OpenAlexafffund
Robert A. Heidemann, Jesper Spinner Madsen, Erling H. Stenby, Simon Ivar Andersen

Bibliographic record

VenueAIChE Journal · 2004
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSolid solutionUNIQUACThermodynamicsWaxComponent (thermodynamics)Phase diagramChemistryRecrystallization (geology)DecanePhase (matter)Materials scienceActivity coefficientPhysical chemistryPhysicsAqueous solutionOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The behavior of the Coutinho UNIQUAC model for solid wax phases has been examined. The model can produce as many mixed solid phases as the number of waxy components. In binary mixtures, the solid rich in the lighter component contains little of the heavier component but the second phase shows substantial amounts of the lighter component dissolved in the heavier solid. Calculations have been performed taking into account the recrystallization of the solid alkanes into a second solid form. The Coutinho UNIQUAC model has been used to describe the lower‐temperature solid phases. The higher‐temperature mixed solid phase has been assumed to be either an ideal solution or to be described by Coutinho's Wilson activity coefficient model. This procedure accounts for more of the known behavior of mixed n‐alkane solids. Comparison is also made with results assuming that all of the solid phases, both high‐temperature and low‐temperature forms, are pure. Model calculations compare well with the data of Pauly et al. for C18 to C30 waxes precipitating from n‐decane solutions. © 2004 American Institute of Chemical Engineers AIChE J, 51: 290–308, 2005

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.399

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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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

Citations12
Published2004
Admission routes2
Has abstractyes

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