MétaCan
Menu
Back to cohort
Record W3093814234 · doi:10.1002/cjce.23900

Kinetic study of carbonylation of ethanol using homogeneous Rh complex catalyst

2020· article· en· W3093814234 on OpenAlexvenueno aff
Lin Xu, Kai Zhang, Muhammad Asif Nawaz, Dianhua Liu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsnot available
Fundersnot available
KeywordsCarbonylationCatalysisChemistryAcetic acidRhodiumMethanolEthanolSolventCarbon monoxideAutoclaveOrganic chemistryInorganic chemistry

Abstract

fetched live from OpenAlex

Abstract Carbonylation of ethanol using homogeneous rhodium complex catalysts is an essential route for the manufacture of propionic acid, as carbonylation of methanol to acetic acid is a large‐scale commercial process that could provide critical insights for the ethanol carbonylation process. The reaction mechanism of ethanol has not been well understood yet, and the high cost of downstream separation due to high water content is still worrying. Consequently, propionic acid was used as solvent to reduce water content in the kinetic experiments carried out in a semibatch autoclave reactor. Homogeneous rhodium was used as complex catalyst with HI as a promoter and propionic acid as solvent. The effects of ethanol, hydroiodic acid, rhodium, and the pressure of carbon monoxide on reaction rate and selectivity of propionic acid were investigated. The reaction mechanism was determined through these batches in the meantime. A kinetic model for ethanol carbonylation was deduced based upon the observations and reaction mechanism. The parameters of the model were regressed and verified with the experimental data. The activation energy was found to be 75.6 kJ·mol −1 . Residual error distribution and a statistical test showed that the kinetic model is reasonable and acceptable.

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 score0.019
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.033
GPT teacher head0.228
Teacher spread0.195 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicCarbon dioxide utilization in catalysisFrench-language works237,207