Hydrotreating of light gas oil using carbon nanotube supported NiMoS catalysts: Kinetic modelling
Bibliographic record
Abstract
Abstract Multi‐walled carbon nanotubes (MWCNTs) were applied as supports for NiMo hydroprocessing catalysts. Rate expressions were developed for an optimum NiMo/MWCNT catalyst to help predict its hydrodesulfurisation (HDS) and hydrodenitrogenation (HDN) activities while varying the operation parameters for coker light gas oil treatment. Power law models were best fit with reaction orders of 2.6 and 1.2, and activation energies of 161 and 82.3 kJ/mol, for the HDS and HDN reactions, respectively. Generalised Langmuir–Hinshelwood models were found to have reaction orders of 3.0 and 1.5, and activation energies of 155 and 42.3 kJ/mol, for the HDS and HDN reactions, respectively. Étude des nanotubes de carbone multiparois (MWCNT) lorsqu'ils servent de supports aux catalyseurs d'hydrotraitement NiMo. Des expressions de vitesse ont été développées pour un catalyseur NiMo/MWCNT optimal pour prédire ses activités d'hydrodésulfurisation (HDS) et d'hydrodénitrogénation (HDN) tout en faisant varier les paramètres d'exploitation pour le traitement du gas‐oil léger de cokéfaction. Les modèles de loi de puissance sont les mieux adaptés avec des ordres de réaction de 2,6 et 1,2 et des énergies d'activation de 161 kJ/mol et 82,3 kJ/mol pour les réactions d'HDS et d'HDN respectivement. Les modèles généralisés de Langmuir–Hinshelwood ont des ordres de réaction de 3,0 et 1,5 et des énergies d'activation de 155 kJ/mol et 42,3 kJ/mol pour les réactions d'HDS et d'HDN respectivement. © 2010 Canadian Society for Chemical Engineering
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".