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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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 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".