Toward Concurrent Engineering of the M1-Based Catalytic Systems for Oxidative Dehydrogenation (ODH) of Alkanes

Gaffney, Anne M.; An, Qi; Goddard, William A.; Diao, Weijian; Glazoff, Michael V.

doi:10.1007/s11244-020-01327-7

Cited by 8 publications

(7 citation statements)

References 83 publications

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“…The reactivity of the multicomponent Mo-V-Te-Nb-O mixed metal oxide catalysts, presenting mainly M1, is likely due only to the reactivity of the vanadium sites in a suitable environment [33][34][35][36][37][38][39][40][41][42][43][44]. In fact, it is known that molybdenum oxide-based catalysts can activate ethane at temperatures over 500 °C [19], which are much higher than the reaction temperature of the present work, whereas tellurium and niobium sites are inert under these reaction conditions.…”

Section: Catalytic Resultsmentioning

confidence: 99%

“…In other words, the M1 phase would be the support that enables the formation of a thin active surface layer that contains Mo 6+ /Mo 5+ /V 4+ /V 5+ in close vicinity to Te 4 + oxo-sites and pentagonal Nb 5+ -containing units [67]. In addition, the presence of Nb atoms in optimized catalysts [33][34][35][36][37][38][39][40][41][42][43][44] has been related to the elimination of Brønsted sites after the incorporation of Nb 5+ into the framework of the catalyst [41].…”

Section: General Remarksmentioning

confidence: 99%

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Evolution of the optimal catalytic systems for the oxidative dehydrogenation of ethane: The role of adsorption in the catalytic performance

Arriba

Solsona

Dejoz

et al. 2022

Journal of Catalysis

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Section: Catalytic Resultsmentioning

confidence: 99%

Section: General Remarksmentioning

confidence: 99%

Evolution of the optimal catalytic systems for the oxidative dehydrogenation of ethane: The role of adsorption in the catalytic performance

Arriba

Solsona

Dejoz

et al. 2022

Journal of Catalysis

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“…Machine learning potential is one of the most critical calculations advances in recent years and has been intensively studied and applied in catalysis [112][113][114][115][116][117][118][119][120][121][122][123][124][125][126][127]. The machine learning potential is a method that uses the machine learning algorithm to find the underneath relationship of the atomic configuration and energy [128].…”

Section: Applications Of Machine Learning In Catalysis 321 Machine Learning Potentialsmentioning

confidence: 99%

Molecular Dynamics and Machine Learning in Catalysts

Liu

Zhu

et al. 2021

Catalysts

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Given the importance of catalysts in the chemical industry, they have been extensively investigated by experimental and numerical methods. With the development of computational algorithms and computer hardware, large-scale simulations have enabled influential studies with more atomic details reflecting microscopic mechanisms. This review provides a comprehensive summary of recent developments in molecular dynamics, including ab initio molecular dynamics and reaction force-field molecular dynamics. Recent research on both approaches to catalyst calculations is reviewed, including growth, dehydrogenation, hydrogenation, oxidation reactions, bias, and recombination of carbon materials that can guide catalyst calculations. Machine learning has attracted increasing interest in recent years, and its combination with the field of catalysts has inspired promising development approaches. Its applications in machine learning potential, catalyst design, performance prediction, structure optimization, and classification have been summarized in detail. This review hopes to shed light and perspective on ML approaches in catalysts.

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“…The commercial M1 catalyst may be scaled up using the approach of concurrent engineering [13]. A synthesis strategy has been proposed for the combining nanoparticles of TeVO4 with clusters of MoNbTeOy to form a single-phase catalyst, in which the vanadium-rich Te-O active sites that activate the alkane to form alkene are properly distinguished by MoNbTeOy zones [14]. This strategy results in a mixed metal oxide catalyst that is useful for both ethane and propane oxidative dehydrogenation.…”

Section: Oxidative Dehydrogenation Catalyst Scale-upmentioning

confidence: 99%

Techno-Economic Comparison Between Conventional Steam Pyrolysis and Oxidative Dehydrogenation of Ethane for the Production of Ethylene

2020

IJESRT

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Facilities based on both Conventional Steam Pyrolysis (CSP) and Oxidative Dehydrogenation (ODH) are methods of producing and separating an alkene, such as ethylene, from an alkane, such as ethane. The CSP system is a time honored and historic process consisting of an endothermic reaction to crack ethane into ethylene followed by a separation system to remove hydrogen and methane. Distillation is then used to purify the ethylene to polymer grade. The ODH method comprises subjecting a feedstock containing ethane to exothermic oxidative dehydrogenation using an M1 catalyst to produce an ethylene stream. The ethylene stream is distilled and/or passed through membrane separators to purify and recover the ethylene. The ODH reaction system comprises at least one tubular reactor, a heat management unit coupled to the ODH reactor, and at least one membrane separation unit comprising a facilitated membrane exchanger. The ODH reactor is configured with tubes packed with the M1 catalyst to convert ethane to ethylene. Additionally, the ethylene produced is reported to be refractory, thus avoiding secondary reactions. The heat management unit is configured to reduce the temperature of the ethylene product stream and recovering the heat of reaction. These processes and the associated economics are discussed. The economic assessment indicates that the ODH route is superior in the three key areas in the cost of production: variable costs, fixed costs and capital recovery.

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Toward Concurrent Engineering of the M1-Based Catalytic Systems for Oxidative Dehydrogenation (ODH) of Alkanes

Cited by 8 publications

References 83 publications

Evolution of the optimal catalytic systems for the oxidative dehydrogenation of ethane: The role of adsorption in the catalytic performance

Evolution of the optimal catalytic systems for the oxidative dehydrogenation of ethane: The role of adsorption in the catalytic performance

Molecular Dynamics and Machine Learning in Catalysts

Techno-Economic Comparison Between Conventional Steam Pyrolysis and Oxidative Dehydrogenation of Ethane for the Production of Ethylene

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