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Record W2054924146 · doi:10.2202/1542-6580.1004

Modeling of the Catalytic Distillation Process for the Synthesis of Ethyl Cellosolve Using A Three-Phase Nonequilibrium Model

2003· article· en· W2054924146 on OpenAlexafffund
Yu-Xiang Zheng, Flora T. T. Ng, Garry L. Rempel

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2003
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDistillationReactive distillationNon-equilibrium thermodynamicsCatalysisChemistryYield (engineering)Mass transferThermodynamicsEthylene oxideChromatographyOrganic chemistryPhysicsPolymer

Abstract

fetched live from OpenAlex

Abstract The catalytic distillation (CD) process for the synthesis of ethyl cellosolve from ethanol and ethylene oxide on molecular sieve catalyst NKC-01 in a 200 mm CD pilot column was simulated using the three-phase nonequilibrium model which was developed in our laboratory. The main feature of this model is that the actual rates for transport and reaction are used and the stage efficiency or HETP (height equivalent to a theoretical plate) is not required. The effect of multicomponent mass and heat transfer between vapor and liquid phases as well as between liquid and solid (catalyst) phases was taken into account according to the Maxwell-Stefan equations. The Newton-Raphson method was used to solve the model equations. The simulation profiles of the temperature and composition along the column are in good agreement with the pilot CD plant data obtained for the synthesis of ethyl cellosolve. Good agreement between model predictions and experimental data is also obtained for the yield and selectivity of the ethyl cellosolve. This model could be extended to simulate other CD processes and commercial scale CD plants.

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)0.000
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: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.275
Teacher spread0.250 · 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

Citations4
Published2003
Admission routes2
Has abstractyes

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