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Record W2747727131 · doi:10.1016/j.egypro.2017.03.1391

Design Method for Layered-bed Adsorption Column for Separation of CO2 and N2 from Natural Gas

2017· article· en· W2747727131 on OpenAlexaff
M Rokanuzzaman, Amornvadee Veawab, Adisorn Aroonwilas

Bibliographic record

VenueEnergy Procedia · 2017
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsAdsorptionColumn (typography)Molecular sieveSieve (category theory)Activated carbonMaterials scienceCarbon fibersChromatographyChemical engineeringChemistryProcess engineeringEngineeringMathematicsStructural engineeringComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

A design method was developed for a layered-bed adsorption column that contains commercial adsorbents for the removal CO2 and N2 including zeolite13X, carbon molecular sieve (CMS3K), and activated carbon (ACB). This design method helped determine the important design parameters as well as the amount of adsorbents needed for the specified removal targets that was eventually translated into column dimensions. The Aspen Adsorption software was used for modeling and simulating double-layered column operation. The required model inputs were gathered from literature, calculated using various equations and correlations, and obtained through fitting experimental data. The model was validated against several adsorption processes that covered various operating conditions on different adsorbents.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.019
GPT teacher head0.273
Teacher spread0.254 · 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
GenreMethods

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

Citations9
Published2017
Admission routes1
Has abstractyes

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