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Record W2613683291 · doi:10.1002/cjce.22890

Dehydration kinetics for coal‐based isobutanol on <i>γ</i>‐Al<sub>2</sub>O<sub>3</sub>

2017· article· en· W2613683291 on OpenAlexvenueno aff
Rongli Mi, Ying-Ming Li, Xueyu Tian, Hailing Peng, Bolun Yang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldChemistry
TopicZeolite Catalysis and Synthesis
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsIsobutanolChemistryKineticsSelectivityCatalysisDehydrationAdsorptionThermodynamicsInorganic chemistryChromatographyPhysical chemistryOrganic chemistryAlcoholBiochemistry

Abstract

fetched live from OpenAlex

Abstract Isobutanol dehydration using γ‐Al 2 O 3 catalysts to produce isobutene was carried out by a fixed bed reactor at 0.1 MPa. The effects of temperature, weight hourly space velocity, and particle size of catalyst on the conversion of isobutanol and selectivity of isobutene were investigated to find the optimum reaction conditions and get the kinetics information. The experimental results indicated that higher temperature could improve the conversion of isobutanol while a peak of selectivity of isobutene can be monitored with the temperature increasing. Meanwhile, increasing the flow rate leads to a decrease in the conversion of isobutanol, but showed little effect on selectivity of isobutene. The intrinsic kinetics model of isobutanol dehydration over γ‐Al 2 O 3 was developed according to the experimental results. The adsorption of isobutanol was considered as a rate‐determining step from the analysis results of reaction mechanism, and kinetics parameters were obtained by Levenberg‐Marquardt algorithm. The calculated results agreed well with the experimental data.

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)0.001
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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.200
Teacher spread0.189 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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