Glycerol Hydrogenolysis to Produce 1,2-Propanediol in Absence of Molecular Hydrogen Using a Pd Promoted Cu/MgO/Al2O3 Catalyst
Bibliographic record
Abstract
The catalytic process of glycerol hydrogenolysis to produce 1,2-propandiol (1,2-PD) in the absence of external hydrogen addition has been investigated. The methanol present in the crude glycerol from a biodiesel production process is used to provide in situ hydrogen produced via methanol steam reforming for the glycerol hydrogenolysis process. This process can reduce the additional cost for the transportation and storage of molecular hydrogen and also reduce the safety risks related to using high hydrogen pressure. It was found that the introduction of Pd onto a Cu/MgO/Al2O3 catalyst significantly improved the glycerol conversion and 1,2-PD selectivity. The pseudo-first-order kinetic results suggested that the promoting effect of Pd is primarily attributed to the enhanced activity for the hydrogenation of acetol, which is the intermediate formed via glycerol dehydration. A 27−3 fractional factorial design experiment was carried out to investigate the impacts of seven single factors and their binary effects on two responses, namely 1,2-PD selectivity and glycerol conversion. The results showed that the glycerol feed concentration has the most significant effect on the 1,2-PD selectivity, such that the 1,2-PD selectivity is lower if a more concentrated glycerol is used as the feedstock; stirring speed, inert gas pressure and water to methanol molar ratio have insignificant effects on the reaction system. The addition of Pd, higher temperature and higher catalyst loading are the essential factors in order to obtain a high selectivity of 1,2-PD and a high glycerol conversion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".