Kinetics of non-oxidative propane dehydrogenation on Cr2O3 and the nature of catalyst deactivation from first-principles simulations

Huš, Matej; Kopač, Drejc; Likozar, Blaž

doi:10.1016/j.jcat.2020.03.037

Cited by 61 publications

(51 citation statements)

References 62 publications

(66 reference statements)

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“…These values were selected based on an extensive literature review 38 − 41 and follows our previous work on propane. 29 A poor description of the dispersion interactions was circumvented using the Grimme D3 method. 42 To obtain the vibrational frequencies of the adsorbates and transition states required for the calculation of the partition functions and zero-point energy (ZPE) correction, the finite difference approach was adopted with a displacement step of 0.01 Å.…”

Section: Methodsmentioning

confidence: 99%

“… 5 , 28 Furthermore, the deactivation of the catalyst by means of coking can irreversibly damage the catalyst, but this can be tackled either by adding the alkaline metal promoters, such as Li, Na, and K, which poison the acidic sites and suppress the formation of coke on the support, 5 or by performing the reaction at lower temperatures (below ∼900 K), where C–C bond cracking is less pronounced. 29 …”

Section: Introductionmentioning

confidence: 99%

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First-Principles-Based Multiscale Modelling of Nonoxidative Butane Dehydrogenation on Cr₂O₃(0001)

et al. 2020

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Propane (C 3 H 8 ) and butane (C 4 H 10 ) are short straight-chain alkane molecules that are difficult to convert catalytically. Analogous to propane, butane can be dehydrogenated to butenes (also known as butylenes) or butadiene, which are used industrially as raw materials when synthesizing various chemicals (plastics, rubbers, etc.). In this study, we present results of detailed first-principles-based multiscale modelling of butane dehydrogenation, consisting of three size- and time-scales. The reaction is modelled over Cr 2 O 3 (0001) chromium oxide, which is commonly used in the industrial setting. A complete 108-step reaction pathway of butane (C 4 H 10 ) dehydrogenation was studied, yielding 1-butene (CH 2 CHCH 2 CH 3 ) and 2-butene (CH 3 CHCHCH 3 ), 1-butyne (CHCCH 2 CH 3 ) and 2-butyne (CH 3 CCCH 3 ), butadiene (CH 2 CHCHCH 2 ), butenyne (CH 2 CHCCH), and ultimately butadiyne (CHCCCH). We include cracking and coking reactions (yielding C 1 , C 2 , and C 3 hydrocarbons) in the model to provide a thorough description of catalyst deactivation as a function of the temperature and time. Density functional theory calculations with the Hubbard U model were used to study the reaction on the atomistic scale, resulting in the complete energetics and first-principles kinetic parameters for the dehydrogenation reaction. They were cast in a kinetic model using mean-field microkinetics and kinetic Monte Carlo simulations. The former was used to obtain gas equilibrium conditions in the steady-state regime, which were fed in the latter to provide accurate surface kinetics. A full reactor simulation was used to account for the macroscopic properties of the catalytic particles: their loading, specific surface area, and density and reactor parameters: size, design, and feed gas flow. With this approach, we obtained first-principles estimates of the catalytic conversion, selectivity to products, and time dependence of the catalyst activity, which can be paralleled to experimental data. We show that 2-butene is the most abundant product of dehydrogenation, with selectivity above 90% and turn-over frequency above 10 –3 s –1 at T = 900 K. Butane conversion is below 5% at such low temperature, but rises above 40% at T > 1100 K. Activity starts to drop after ∼6 h because of surface poisoning with carbon. We conclude that the dehydrogenation of butane is a viable alternative to conventiona...

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

First-Principles-Based Multiscale Modelling of Nonoxidative Butane Dehydrogenation on Cr₂O₃(0001)

et al. 2020

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“…It was found that it can be decomposed into two contributions: the formation of C* and the formation of CH 3 CC*. The overall deactivation can be described with Arrhenius-like kinetics with an apparent barrier of 2.82 eV [13].…”

Section: Coke Formation and Catalyst Deactivationmentioning

confidence: 99%

“…Propylene is mainly obtained as a by-product of steam cracking and fluid catalytic cracking of naphtha, light diesel, and other oil products [11,12]. It is the second most important chemical in the petrochemical industry and used for the production of polypropylene, propylene oxide, acrylic acid and acrylonitrile [13]. Since the 1930s, due to the rapid increase in the demand for propylene, it has been produced with the direct catalytic dehydrogenation of propane [11,13].…”

Section: Introductionmentioning

confidence: 99%

“…It is the second most important chemical in the petrochemical industry and used for the production of polypropylene, propylene oxide, acrylic acid and acrylonitrile [13]. Since the 1930s, due to the rapid increase in the demand for propylene, it has been produced with the direct catalytic dehydrogenation of propane [11,13]. Pt is the most commonly used catalyst for the propane dehydrogenation process (PDH) despite its propensity for coking.…”

Section: Introductionmentioning

confidence: 99%

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Ab Initio Multiscale Process Modeling of Ethane, Propane and Butane Dehydrogenation Reactions: A Review

et al. 2020

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Olefins are among the most important structural building blocks for a plethora of chemical reaction products, including petrochemicals, biomaterials and pharmaceuticals. An ever-increasing economic demand has urged scientists, engineers and industry to develop novel technical methods for the dehydrogenation of parent alkane molecules. In particular, the catalysis over precious metal or metal oxide catalysts has been put forward as an alternative way route to thermal-, steam- and fluid catalytic cracking (FCC). Multiscale system modeling as a tool to theoretically understand processes has in the past decade period evolved from a rudimentary measurement-complementing approach to a useful engineering environment. Not only can it predict various experimentally obtained parameters, such as conversion, activity, and selectivity, but it can help us to simulate trends, when changing applicative operating conditions, such as surface gas temperature or pressure, or even support us in the search for the type of materials, their geometrical properties and phases for a better functional performance. An overview of the current set state of the art for saturated organic short chain hydrocarbons (ethane, propane and butane) is presented. Studies that combine at least two different dimensional scales, ranging from atomistic-, bridging across mechanistic mesoscale kinetics, towards reactor- or macroscale, are focused on. Insights considering reactivity are compared.

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Machine Learning Enabled Screening of Single Atom Alloys: Predicting Reactivity Trend for Ethanol Dehydrogenation

et al. 2021

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A machine learning (ML) approach implementing the gradient boosting regressor (GBR) algorithm is applied to predict the binding energies of oxygen (E O ) and carbon (E C ) atoms on single atom alloys (SAAs) of Cu, Ag and Au. Readily available periodic properties of the transition metals are utilized as input features in the model. Their relative contribution in adsorbate-metal interaction is assessed to develop a comprehensive descriptor. In test runs, the ML model is observed to predict E O and E C with significantly reduced errors (~0.2 eV). Further, ML approach is augmented with an ab initio microkinetic model (MKM) for non-oxidative dehydrogenation (NODH) of ethanol. The ML-MKM is calculated to yield higher turnover frequency for ethanol conversion on NiAu, NiAg, and PtAg SAAs as compared to their monometallic counterparts Au and Ag catalysts. Overall, the ML model provides a rationale for feature selection to synthesize catalytically active SAAs of group 11 elements using fast-track in silico screening.

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Kinetics of non-oxidative propane dehydrogenation on Cr2O3 and the nature of catalyst deactivation from first-principles simulations

Cited by 61 publications

References 62 publications

First-Principles-Based Multiscale Modelling of Nonoxidative Butane Dehydrogenation on Cr₂O₃(0001)

First-Principles-Based Multiscale Modelling of Nonoxidative Butane Dehydrogenation on Cr₂O₃(0001)

Ab Initio Multiscale Process Modeling of Ethane, Propane and Butane Dehydrogenation Reactions: A Review

Machine Learning Enabled Screening of Single Atom Alloys: Predicting Reactivity Trend for Ethanol Dehydrogenation

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Kinetics of non-oxidative propane dehydrogenation on Cr2O3 and the nature of catalyst deactivation from first-principles simulations

Cited by 61 publications

References 62 publications

First-Principles-Based Multiscale Modelling of Nonoxidative Butane Dehydrogenation on Cr2O3(0001)

First-Principles-Based Multiscale Modelling of Nonoxidative Butane Dehydrogenation on Cr2O3(0001)

Ab Initio Multiscale Process Modeling of Ethane, Propane and Butane Dehydrogenation Reactions: A Review

Machine Learning Enabled Screening of Single Atom Alloys: Predicting Reactivity Trend for Ethanol Dehydrogenation

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First-Principles-Based Multiscale Modelling of Nonoxidative Butane Dehydrogenation on Cr₂O₃(0001)

First-Principles-Based Multiscale Modelling of Nonoxidative Butane Dehydrogenation on Cr₂O₃(0001)