Syntheses, characterization and kinetics of nickel-tungsten nitride catalysts for \nhydrotreating of gas oil
Bibliographic record
Abstract
This thesis summarizes the methods and major findings of Ni-W(P)/γ-Al 2 O 3The range of parameters for catalyst synthesis were W (15-40 wt%), Ni (0-8 wt%), P (0-5 wt%) and nitriding temperature (T nitride catalyst synthesis, characterization, hydrotreating activity, kinetic analysis and correlation of the catalysts' activities to their synthesis parameters and properties.N ) (500-900 °C).Characterization techniques used included: N 2 sorption studies, chemisorption, elemental analysis, temperature programmed studies, x-ray diffraction, scanning electron microscopy, energy dispersive x-ray, infrared spectroscopy, transmission electron microscopy and x-ray absorption near edge structure.Hydrodesulfurization (HDS), hydrodenitrogenation (HDN) and hydrodearomatization (HDA) were performed at: temperature (340-380 °C), pressure (6.2-9.0MPa), liquid hourly space velocity (1-3 h -1The predominant species on the catalyst surface were Ni ) and hydrogen to oil ratio (600 ml/ml, STP).ACKNOWLEDGEMENT I wish to acknowledge the input and contribution of a number of persons who helped in diverse ways to bring this thesis to fruition.First and foremost, I render my sincere gratitude to my supervisors, Drs A. K. Dalai and J. Adjaye for their invaluable assistance
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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".