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Record W2064336761 · doi:10.1002/cjce.5450820510

Dehydration of Methanol to Dimethyl Ether by Catalytic Distillation

2004· article· en· W2064336761 on OpenAlexaffvenue
Weizhu An, Karl T. Chuang, Alan R. Sanger

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDimethyl etherMethanolChemistryDehydrationDistillationCatalysisKineticsReactive distillationBatch distillationDehydration reactionOrganic chemistryChemical engineeringFractional distillation

Abstract

fetched live from OpenAlex

Abstract The kinetics of liquid catalytic dehydration of methanol over an ion exchange resin (Amberlyst 35) has been determined for the temperature range 343 to 403 K using a batch reactor. The experimental data are described well by an Eley‐Rideal type kinetic expression, for which the surface reaction is the rate‐determining step. A catalytic distillation process for methanol dehydration to dimethyl ether (DME) has been modeled using the experimentally determined kinetic data. The results were incorporated into the rate‐controlled reaction mode for RadFrac, a part of the commercial simulation program Aspen Plus. It was shown that synthesis of high purity DME can be achieved using a single catalytic distillation column. Thus there is significant potential for reduction of overall capital cost for a plant for methanol dehydration to DME when compared to conventional production facilities that involve separate reaction and distillation processes.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.182
Teacher spread0.177 · 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 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

Citations54
Published2004
Admission routes2
Has abstractyes

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