The effects of modes of hydrogen input and reactor configuration on reaction rate and H<sub>2</sub> efficiency in the catalytic hydrogenation of alkynol to alkenol
Bibliographic record
Abstract
Abstract Hydrogenation often involves three phases where hydrogen‐on‐demand is the typical mode of operation in industrial scale reactors. In research labs and publications, however, continuous hydrogen flow has been used. This paper investigates the effect of such modes of operation on reaction rate using a selective hydrogenation of 3‐butyn‐2‐ol over Pd/Al2O3 to obtain 3‐buten‐2‐ol as the model reaction. The two modes of operation were first tested in a commercial PARR stirred tank reactor and then repeated in an oscillatory baffled reactor (OBR) in order to validate the experimental results. Our investigation demonstrates that an enhanced reaction performance and 10 times better H2 efficiency were obtained when the pressure was maintained constant during the reaction by feeding gas as required, ie hydrogen‐on‐demand mode. The method of a continuous flow of hydrogen in hydrogenation means that excess hydrogen is vented out when operating at ambient pressures or builds up at elevated pressures. Our work also enables a comparison of reactor designs on reactor performance, and three times higher H2 efficiency and 2.3 times shorter residence time were achieved when using the OBR instead of the PARR due to its enhanced and uniform mixing, regardless of the mode of operation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".