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Record W4414859350 · doi:10.1021/acs.iecr.5c02209

Numerical Study on N<sub>2</sub> Removal from the N<sub>2</sub>/He Mixture by Supersonic Separation Technology

2025· article· en· W4414859350 on OpenAlexaboutno aff
Baosheng Chen, Yupei Zeng, Nan Peng, Ercang Luo

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2025
Typearticle
Languageen
FieldMathematics
TopicGas Dynamics and Kinetic Theory
Canadian institutionsnot available
FundersNational Key Research and Development Program of China
KeywordsSupersonic speedNozzleNucleationCondensationNon-equilibrium thermodynamicsHeliumNitrogenInlet

Abstract

fetched live from OpenAlex

The application of supersonic separation technology in extracting helium from the N 2 /He mixture is the primary innovation of this work, holding noteworthy promise. To begin with, a numerical model is constructed, incorporating governing equations for gas–liquid phase and the condensation model. The study reveals that nitrogen nonequilibrium condensation can be accomplished using the Laval nozzle, demonstrating the potential for N 2 /He separation in the supersonic separator. Enhancing nitrogen nonequilibrium condensation within the Laval nozzle can be achieved by lowering the inlet temperature and raising the inlet pressure. Results show that nitrogen experiences intensive homogeneous nucleation over a short duration in the divergent section, leading to an ongoing rise in the nitrogen liquid mass fraction from 0% at the inlet to 10.5% at the outlet. This research verifies the viability of employing the Laval nozzle for nitrogen removal, carrying significant strategic implications for advancing nitrogen removal technologies in the recovery of helium from natural gas.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0010.003
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 head0.043
GPT teacher head0.338
Teacher spread0.296 · 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.

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

Citations2
Published2025
Admission routes1
Has abstractyes

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