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Record W2018434268 · doi:10.1021/ie0400144

Measurement and Prediction of the Phase Behavior of Wax−Solvent Mixtures:  Significance of the Wax <i>Dis</i>appearance Temperature

2004· article· en· W2018434268 on OpenAlexafffund
Nitin V. Bhat, Anil K. Mehrotra

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2004
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsWaxLiquidusSolubilityChemistryUNIQUACSolventChromatographyPhase (matter)ThermodynamicsAnalytical Chemistry (journal)Organic chemistryAqueous solutionActivity coefficient

Abstract

fetched live from OpenAlex

This study investigates the phase behavior of wax−solvent mixtures below their liquidus temperature, particularly between the wax disappearance temperature (WDT, recorded while heating the mixture) and the wax appearance temperature (WAT, recorded while cooling the mixture). The prepared wax−solvent mixtures comprised 6−22 mass % of a paraffinic wax (C 20 −C 40, with the mean carbon number of 28) dissolved in n -hexadecane (C 16 ) and Norpar13 (a paraffinic solvent, C 11 −C 15 ). For these mixtures, the WAT values were lower than the WDT values by 3.2 ± 0.6 °C. Gas chromatograph analyses of the liquid phase, at temperatures between the WDT and the pour-point temperature, were used to calculate the wax solubility and the composition of the dissolved wax. At temperatures below the WDT, the wax solubility decreased with decreasing temperature, and the liquid-phase concentrations of C 20 −C 27 were higher than those in the whole wax and those of C 29 + were lower. The experimental data compared satisfactorily with predictions from an available UNIQUAC-based model for the liquidus temperature as well as for the composition of C 20 + constituents in the liquid phase. This study establishes that the WDT rather than the WAT is closer to the liquidus or saturation temperature of “waxy” mixtures.

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)0.001
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 score0.002
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.055
GPT teacher head0.297
Teacher spread0.241 · 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

Citations58
Published2004
Admission routes2
Has abstractyes

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