MétaCan
Menu
Back to cohort
Record W2323005989 · doi:10.1021/ie500840d

Hydrotreating of Heavy Gas Oil on Mesoporous Mixed Metal Oxides (M–Al<sub>2</sub>O<sub>3</sub>, M = TiO<sub>2</sub>, ZrO<sub>2</sub>, SnO<sub>2</sub>) Supported NiMo Catalysts: Influence of Surface Acidity

2014· article· en· W2323005989 on OpenAlexafffund
Sandeep Badoga, Rajesh Sharma, Ajay K. Dalai, John Adjaye

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsSyncrude (Canada)University of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaSyncrude
KeywordsHydrodesulfurizationCatalysisMesoporous materialHydrodenitrogenationFourier transform infrared spectroscopyInorganic chemistryIncipient wetness impregnationMetalChemistryMaterials scienceOxideNuclear chemistryChemical engineeringSelectivityOrganic chemistry

Abstract

fetched live from OpenAlex

Mesoporous mixed metal oxides, TiO 2 –Al 2 O 3, ZrO 2 –Al 2 O 3, and SnO 2 –Al 2 O 3, were synthesized using a novel method and were used as a support material for the NiMo hydrotreating catalyst. The catalyst was prepared via the incipient wetness sequential impregnation method. All catalysts were characterized using N 2 adsorption–desorption isotherms (BET), X-ray diffraction, FTIR, pyridine-FTIR, acridine-FTIR, CO-chemisorption, ICP-MS, TPD, and TPR. The HDS and HDN activities of the catalysts were determined using Athabasca bitumen derived heavy gas oil at industrial reaction conditions. The catalytic activity of synthesized NiMo/γ-Al 2 O 3 catalyst was also determined for comparative studies. Low angle XRD and BET analysis has confirmed that the method developed for synthesis is suitable for the preparation of a mesoporous mixed oxide based NiMo hydrotreating catalyst. H 2 -TPR analysis has confirmed that the introduction of metal oxides such as ZrO 2, TiO 2, and SnO 2 in alumina increases the active metal (Mo) and support (alumina) interactions. The highest reduction temperature is observed in the SnO 2 containing catalyst. NH 3 -TPD has confirmed the increase in acidic strength of catalysts after introduction of metal oxides, and it follows the order NiMo/SnO 2 –Al 2 O 3 > NiMo/ZrO 2 –Al 2 O 3 > NiMo/TiO 2 –Al 2 O 3 . It was observed from acridine-FTIR analysis that the catalysts having strong acidic strength hold acridine strongly at high temperatures. This implies that catalysts with higher acidity are prone to inhibition by nitrogen containing compounds present in feed, and it will affect the catalytic activity. The HDS and HDN activities follow the order NiMo/TiO 2 –Al 2 O 3 > NiMo/mesoAl 2 O 3 (mesoporous) > NiMo/ZrO 2 –Al 2 O 3 > NiMo/γ-Al 2 O 3 > NiMo/SnO 2 –Al 2 O 3 . The higher activity of the NiMo/TiO 2 –Al 2 O 3 catalyst is due to higher metal dispersion as observed from CO-chemisorption and a moderate increase in acidic strength. Therefore, increasing the acidity of alumina support to much higher levels by incorporating metal oxides having strong Lewis acidity would not help in enhancing HDN and HDS activity for nitrogen rich feeds, such as Athabasca bitumen derived heavy gas oil due to inhibition effects.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.029
GPT teacher head0.259
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations44
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueIndustrial & Engineering Chemistry ResearchSame topicCatalysis and Hydrodesulfurization StudiesFrench-language works237,207