The influence of catalyst acidity and dispersion on the hydrogenation and dimerization of conjugated olefins over NiMoS catalysts
Bibliographic record
Abstract
Crude oil derived from Canadian oil sands has a propensity for coke formation during hydrotreatment over NiMoS catalysts, due to the high olefin content of the oil. This dissertation investigates the influence of catalyst acidity and Mo dispersion on the hydrogenation and dimerization of conjugated olefins at low temperature (< 250℃). Olefin dimerization is the precursor to gum and coke formation that deactivates the NiMoS catalysts. The study aims to understand the relationships between catalyst properties and dimer formation, and thereby improve olefin hydrogenation and reduce olefin dimerization activity of the catalysts. In the first part of the study, NiMoS was dispersed on SiO₂-Al₂O₃ supports with varying Si/Al ratio to determine the impact of catalyst acidity on the dimerization of 4-methylstyrene. The results showed that dimer formation correlated with the acidity of the catalysts. The dimer yield on the sulphided supports without NiMoS, was about 40% of the yield observed on the NiMoS/SiO₂-Al₂O₃ catalysts. Addition of 3wt% NaOH to the NiMoS/S10 catalyst neutralized 60% of the acid sites, resulting in a 50% decrease in dimerization yield on this catalyst. Subsequently, the SiO₂-Al₂O₃ support (10wt% SiO₂) was used to prepare NiMoS catalysts, with varying Mo (3 – 15 wt%), Ni (1.1 – 3.3 wt%) and P (0 – 4 wt%) content. The acidity of the catalysts was relatively constant as the Mo loading increased, whereas in the case of P, the catalyst acidity increased and then declined above 2wt% P. Hence, the highest 4-methylstyrene hydrogenation and lowest dimerization activities were obtained at a composition of 2.2 Ni wt%, Mo 10wt% and 1-2wt% P. The hydrogenation and dimerization kinetics of 4-methylstyrene were also assessed. After accounting for possible deactivation effects, the hydrogenation and dimerization rate constants (per mass of catalyst) for the various catalysts were normalized to the number of hydrogenation and dimerization sites, respectively. However, both rate constants unexpectedly declined as the SiO₂ content of the supports increased and were not constant as the Mo and P content of the catalyst changed. Several possible explanations were proposed to explain these trends and further studies are required to determine their validity.
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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".