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Record W1974973301 · doi:10.1002/cjce.21721

Modelling of multicomponent gas separation with asymmetric hollow fibre membranes—methane enrichment from biogas

2012· article· en· W1974973301 on OpenAlexaffvenueabout
Prodip K. Kundu, A. Chakma, Xianshe Feng

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBiogasMethaneMembranePermeationGas separationNatural gasMembrane technologyChemical engineeringChemistryProcess engineeringWaste managementMaterials scienceEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A mathematical model for the dynamic performance of gas separation with high flux, asymmetric hollow fibre membranes was developed considering the permeate pressure build‐up inside the fibre bore and cross flow pattern with respect to the membrane skin. The solution technique provides reliable examination of pressure and concentration profiles along the permeator length (both residue/permeate streams) with minimal effort. The proposed simulation model and scheme were validated with experimental data of gas separation from literature. The model and solution technique were applied to investigate dynamic performance of several membrane module configurations for methane recovery from biogas (landfill gas or digester gas), considering biogas as a mixture of CO 2 , N 2 and CH 4 . Recycle ratio plays a crucial role, and optimum recycle ratio vital for the retentate recycle to permeate and permeate recycle to feed operation was found. From the concept of two recycle operations, complexities involved in the design and operation of continuous membrane column were simplified. Membrane permselectivity required for a targeted separation to produce pipeline quality natural gas by methane‐selective or nitrogen‐selective membranes was calculated. © 2012 Canadian Society for Chemical Engineering

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 score0.554
Threshold uncertainty score0.510

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 head0.017
GPT teacher head0.202
Teacher spread0.185 · 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

Citations21
Published2012
Admission routes3
Has abstractyes

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