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Record W2891113420 · doi:10.1002/aic.16408

An analytical method of estimating diffusion coefficients of gases in liquids from pressure decay tests

2018· article· en· W2891113420 on OpenAlexafffund
Zehao Yang, Steven L. Bryant, Mingzhe Dong, Hassan Hassanzadeh

Bibliographic record

VenueAIChE Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Calgary
FundersCanada Excellence Research Chairs, Government of CanadaMitacsNational Natural Science Foundation of China
KeywordsDiffusionExponential functionThermodynamicsApplied mathematicsMeasure (data warehouse)Statistical physicsMathematicsPhysicsComputer scienceMathematical analysis

Abstract

fetched live from OpenAlex

We present an exact solution of quasi‐equilibrium model, based on pressure decay technique, to measure diffusion coefficient. The results of the quasi‐equilibrium model are compared with the traditional equilibrium model and it was found that the latter is only a special case of former one. We provide new approximate solution to estimate diffusion coefficient that is more convenient to use, compared with the previously reported approaches. It also reveals that our solution is capable of taking into account greater portion of the collected pressure decay test data and is more accurate. Based on the developed solution, estimation approaches, including linear method and exponential method, are presented and applied to analyze synthetic and experimental pressure‐decay data. The quasi‐equilibrium model is also compared with the traditional equilibrium model. The results reveal that analysis of the test data using equilibrium model may introduce large error in estimation of diffusion coefficient. © 2018 American Institute of Chemical Engineers AIChE J , 65: 434–445, 2019

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 score0.831
Threshold uncertainty score0.351

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 head0.015
GPT teacher head0.315
Teacher spread0.300 · 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

Citations26
Published2018
Admission routes2
Has abstractyes

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