Kinetic study of carbonylation of ethanol using homogeneous Rh complex catalyst
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".