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Record W2317573240 · doi:10.1515/ijcre-2014-0035

Mathematical Modeling, Verification and Optimization for Catalytic Membrane Esterification Micro-reactor

2014· article· en· W2317573240 on OpenAlexaff
Elham El‐Zanati, Stephen M. Ritchie, Heba Abdallah, S.S.E.H. Elnashaie

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsYield (engineering)Membrane reactorCatalysisMembraneReuseMATLABScalingProcess (computing)Design of experimentsProcess engineeringChemical engineeringBiological systemMaterials scienceResponse surface methodologyMicroreactorChemistryComputer scienceEngineeringChromatographyMathematicsOrganic chemistryWaste managementComposite material

Abstract

fetched live from OpenAlex

Abstract Analysis of efficient production of ethyl acetate utilizing a C atalytic M embrane M icro- R eactor (CMMR) was theoretically investigated and verified using published results for the esterification reaction. Grafted sulfonic groups in the pores of a polyethersulfone membrane catalyzed the reaction. Theoretical analysis of the catalytic membrane reactor was achieved through development of a lumped parameter model to describe the CMMR behavior and performance. The developed model was solved numerically for different design and operating conditions using MATLAB Simulink software. The model parameters were verified and validated using the experimental results to achieve a reliable tool for design, replication, scaling-up, and optimization. The approach to maximum conversion was simulated. Cumulative yield per unit time was investigated to determine the optimum process time. Membrane regeneration was conducted and the regeneration time was determined as well in order to reuse the membrane for other cycles. Reactor scaling-up was studied using the model for process design.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.813
Threshold uncertainty score0.632

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 head0.012
GPT teacher head0.238
Teacher spread0.226 · 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 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
Published2014
Admission routes1
Has abstractyes

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