Activity and Selectivity of Novel Structured Pd-Catalysts: Kinetics Modeling of Vegetable Oils Hydrogenation
Bibliographic record
Abstract
Hydrogenation of sunflower oil over novel structured catalysts with pore size ranging from 3 to 20 nm, BET-specific surface area of 710-1200 m²/g, and catalyst metal loadings ranging from 0.7 to 5.0 % w/w, was investigated and compared to a commercial Ni-catalyst. The activity and selectivity of the catalysts as well as profiles of the reaction products such as trans fatty acids (TFA) of the hydrogenated oils were investigated. Surface characteristics of the support and the metal loading significantly affected the activity and selectivity of the Pd-catalysts. Catalyst supports with the pore diameter between 7-8 nm were more active than supports with lower pore diameters. The activity and selectivity of hydrogenation depended on Pd content, with maxima in the concentration range of 0.8 to 1.2 % w/w.The catalyst with Pd-loading of 1% w/w, supported on structured silica material was active and selective for the hydrogenation of sunflower and canola oils under mild process conditions. For same iodine value (IV) reduction, this catalyst produced about the same level of TFA, but produced less stearic acid and was more selective towards cis monoenes formation than Ni-catalyst. More importantly, this catalyst produced a reduced level of stearic acid which causes waxy mouth feel of the hydrogenated fat at increased levels.The lumped kinetic model of the hydrogenation of vegetable oils over Pd as well as Ni catalysts was found useful in the prediction of the fate of reaction and product lumps and was statistically robust. The uncertainty and confidence joint regions were estimated using the Bootstrap Monte Carlo technique.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".