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Record W7038083222

A Gibbs free energy model of phase equilibrium including gas hydrates

2022· article· en· W7038083222 on OpenAlexaboutno aff

Bibliographic record

VenueVirtual Community of Pathological Anatomy (University of Castilla La Mancha) · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSubterranean biodiversity and taxonomy
Canadian institutionsnot available
Fundersnot available
KeywordsFugacityClathrate hydrateHydrateGibbs free energyEquation of statevan der Waals forceWork (physics)Phase (matter)
DOInot available

Abstract

fetched live from OpenAlex

Gas hydrates is shorthand for clathrate hydrates of natural gas, which are solid, crystalline molecular complexes formed from mixtures of water and low molecular weight compounds. \nThis work presents a model of the Gibbs Free Energy of Mixing (GFEM) of hydrates, used in the framework of multiphase equilibrium calculations by means of the minimization of the GFEM. Such an approach was previously used for phase equilibrium calculations involving the three states of matter - \nnamely, solid, liquid and vapor [1] - and has been improved in this work to also account for the hydrate phase. \nThe GFEM for the hydrate phase has been expressed as suggested by Englezos and Bishnoi [2], who focused on the phase behavior of the methane-water system with respect to gas hydrate formation. The GFEM for the hydrate phase combines the fugacity of the guest component and the chemical potential of water in the hydrate phase, calculated using the model first presented by van der Waals and Platteeuw [3]. In this work, the analytic fourth-order Equation of State (EoS) proposed by Yokozeki [4] has been used for the fugacity in the solid, liquid and vapor phases. This EoS offers the advantage of describing the three states of matter simultaneously with the same Equation of State, without using a different approach for the solid phase [5,6]. \nThe proposed model, with properly regressed parameters, has been applied to the systems methane-water and hydrogen sulfide-water, which are important in industrial practice in many hydrocarbon reservoirs, especially in Alberta [7]. In order to show the reliability of the proposed approach, which can be easily extended to multicomponent systems, the results of calculations for the \nhydrate formation pressures are compared with both the experimental data available in the literature and the results obtained using commercially available hydrate prediction programs that make use of different approaches.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0040.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.078
GPT teacher head0.226
Teacher spread0.148 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes1
Has abstractyes

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