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Synthesis of carbon nanotubes supported iron catalysts for light olefins via Fischer-Tropsch synthesis

2023· dissertation· en· W6996775460 sur OpenAlexfundno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2023
Typedissertation
Langueen
DomaineChemical Engineering
ThématiqueCatalysts for Methane Reforming
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaUniversity of Saskatchewan
Mots-clésCatalysisSyngasSelectivityCarbon nanotubeHydrocarbonAcetyleneYield (engineering)Olefin fiber
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Light olefins including ethylene, propylene and butylene are the basics of many chemical products. As the demand for light olefins is dramatically increased and oil resources are limited, it becomes desirable to produce light olefins from other resources such as syngas. Syngas could be obtained from alternative feedstocks such as methane, coal, biomass, and plastic wastes. Fischer-Tropsch (FT) synthesis involves conversion of syngas to hydrocarbons. FT products at high temperatures are mainly gasoline and light olefins. In FT catalytic reaction, iron is preferred due to its low cost, high selectivity towards olefins and flexibility in terms of use for different ratio of H2 to CO in syngas feed. In this study, catalytic performance of iron catalyst was evaluated using molybdenum and potassium as promoters and carbon nanotubes (CNTs) as support. The study plan for this research was divided into four sub-objectives or phases.
\nIn the first phase, catalytic chemical vapor deposition (CCVD) method was applied to synthesize CNTs using Fe/CaCO3 and acetylene as catalyst and hydrocarbon source, respectively. Applying response surface methodology, the optimum operating conditions were determined in CVD reactor for maximal yield and purity of CNTs. The effects of reaction time (30–60 min), reaction temperature (700–800 °C), and loading of the catalyst (10–30 wt% Fe) were investigated. 20Fe/CNTs-synthesized, 20Fe/CNTs-commercial, and 20Fe/Al2O3 were analyzed in terms of physio-chemical properties and FTS catalytic performance. The catalytic performance of Fe-based catalysts was investigated using a fixed-bed reactor at 280 °C under 2.0 MPa. 20Fe/CNTs-synthesized exhibited a lower rate of water-gas-shift (WGS) reaction compared with 20Fe/CNTs-commercial, with C2-C4 selectivity of 23.6% which is slightly less than that of its commercial counterpart. After 120 h time-on-stream under steady state condition, the higher activity was maintained by the 20Fe/CNTs-synthesized catalyst compared to the 20Fe/CNTs-Commercial and 20Fe/Al2O3 catalysts.
\nElectronic structural promoters such as K and Mo improve olefins’ selectivity and catalytic activity. Hence, in the second phase, CNTs synthesized by CCVD were used as support to obtain K- and/or Mo-promoted Fe/CNTs catalysts for light olefins’ production in FTS. A two-level full factorial design was applied for K- and/or Mo-promoted Fe/CNTs catalyst to investigate the effects of synthesis conditions including Mo/K mass ratio, ultrasonic time, and iron loading on light olefins’ yield. CO chemisorption and TEM revealed that molybdenum plays a significant role in metal dispersion, leaving structural defects on CNTs support.
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\nAdditionally, H2-TPR confirmed that K as promoter facilitates reducibility of Fe/CNTs catalysts, which promoted CO conversion in FTS. Compared with the un-promoted Fe/CNTs catalysts, addition of molybdenum as a promoter increased light olefins' selectivity by 33.4%, while potassium led to an increase in CO conversion by 96.3%. The optimum formulation (0.5K5Mo10Fe/CNTs) obtained the olefins’ yield of 35.5%.
\nIn the third phase the kinetic study of FTS was performed over the optimum bimetallic promoted catalyst (0.5K5Mo10Fe/CNTs) in a fixed-bed reactor by collecting experimental data over a wide range of industrially relevant reaction conditions (P = 0.68–4.13 MPa, T = 270-290 °C, H2/CO = 1, GHSV = 2000 h-1). Based on the adsorption of carbon monoxide and hydrogen, twenty-two possible mechanisms for monomer formation during Fischer-Tropsch synthesis were proposed in accordance with the Langmuir-Hinshelwood-Hougen-Watson (LHHW) and Eley-Rideal (ER) adsorption theories. Kinetic parameters such as activation energy, adsorption enthalpies of H2 and CO were estimated to be 65.0, -13.0, and -54.0 kJ/mol, respectively. Based on the developed kinetic model, the effects of reaction temperature and pressure were assessed on FTS product distribution. In addition, the Anderson-Schulz-Flory model was applied to further assess the reliability of the best fit mechanistic model for a wide range of hydrocarbon products.
\nIn the fourth phase, techno-economic analysis (TEA) and life cycle assessment (LCA) of light olefin production in Fischer-Tropsch synthesis reaction were investigated via different scenarios. Data from a lab-scale experiment using the optimum bimetallic promoted catalyst (0.5K5Mo10Fe/CNTs) were used to simulate a plant to produce 1 kg of ethylene/h. The economic feasibility of light olefins production was estimated based on a comprehensive cash flow analysis. The net rate of return (NRR) was calculated to 5.6%, 7.4%, and 18.2% for the base scenario (scenario 1), scenario 2 with wastewater treatment, and scenario 3 with wastewater treatment-separation unit, respectively, which means the project is profitable from an economic perspective. The GHG emissions performance was measured as 77.5 g CO2-eq per MJ ethylene confirming the significant GHG emissions decrease compared to petroleum-based fuels production (3686 g CO2-eq per MJ ethylene).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,057
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,006
Tête enseignante GPT0,176
Écart entre enseignants0,170 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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