Catalyst screening for the oxidative coupling of methane: from isothermal to adiabatic operation <i>via</i> microkinetic simulations

Pirro, Laura; Mendes, Paulo; Vandegehuchte, Bart D.; Marin, Guy; Thybaut, Joris W.

doi:10.1039/c9re00478e

Cited by 18 publications

(15 citation statements)

References 54 publications

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“…Many research efforts in industry and academia have been devoted to the simulation of the time evolution, thus kinetics of processes with several species or entities that play a crucial role in research fields such as (bio)chemical engineering, (bio)chemistry, biology, and pharmacology. These processes are determined by, for instance, chemical reactions (e.g., catalyst synthesis and degradation events), purification operations (e.g., water filtration/purification), diffusion (e.g., the spreading of viruses), and pathologies (e.g., Alzheimer’s disease).…”

Section: Introductionmentioning

confidence: 99%

Gillespie-Driven kinetic Monte Carlo Algorithms to Model Events for Bulk or Solution (Bio)Chemical Systems Containing Elemental and Distributed Species

Trigilio

Marien

Steenberge

et al. 2020

Ind. Eng. Chem. Res.

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Stochastic modeling techniques have emerged as a powerful tool to study the time evolution of processes in many research fields including (bio)chemical engineering and biology. One of the most applied techniques is kinetic Monte Carlo (kMC) modeling according to the stochastic simulation algorithm (SSA) as pioneered by Gillespie, in which MC channels and time steps are discretely sampled from probability distributions. In the last decades, the SSA algorithm, as originally developed for systems with elemental species (e.g., A, B, C, etc.), has been further adapted (i) to also tackle systems with distributed species, therefore, populations and (ii) to enable faster algorithm execution. In the present contribution, we highlight the most important developments, taking bulk/solution polymerization as the reference distributed chemical process. We address SSA principles based on conventional array data structures, common acceleration methods (e.g., τ leaping and the scaling method), and the strength of tree- and matrix-based data structures for detailed storage of molecular information per distribution type and even individual population member. In addition, we report advancement regarding array programming and MC sampling methods complemented by the introduction of the use of higher-order trees and root-finding sampling tools. The contribution gives thus a detailed overview of the available main kMC algorithm steps to study kinetics, irrespective of the specific field of application due to their generic nature.

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Section: Introductionmentioning

confidence: 99%

Gillespie-Driven kinetic Monte Carlo Algorithms to Model Events for Bulk or Solution (Bio)Chemical Systems Containing Elemental and Distributed Species

Trigilio

Marien

Steenberge

et al. 2020

Ind. Eng. Chem. Res.

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“…23 The same authors have recently used a similar statistical based approach to select/fit catalysts for adiabatic reactor operation. 24 In this work, we use a statistical approach to identify key catalyst properties related to composition, morphology, or performance that show correlations with estimated kinetic parameters. A series of catalysts with varying composition of Mn 2 O 3 and Na 2 WO 4 were synthesized and fully characterized.…”

Section: ■ Introductionmentioning

confidence: 99%

Correlating Properties of the Mn₂O₃–Na₂WO₄/SiO₂Catalyst with Statistically Estimated Parameters for the Oxidative Coupling of Methane

et al. 2021

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Identifying key catalyst parameters that govern catalytic performance is a main challenge for many reactions. The complex and convoluted behavior of the Mn 2 O 3 −Na 2 WO 4 /SiO 2 catalyst for the oxidative coupling of methane (OCM) makes this task even more challenging. Herein, structure−function correlations are obtained using a simplified methodology that involves cross-referencing statistically estimated reaction kinetic parameters with various experimentally measured catalyst and reaction properties. These correlations and conclusions are shown to be consistent with literature data, which was obtained using advanced in situ techniques. Specifically, these correlations highlight the importance of maintaining highly dispersed Mn 2 O 3 particles in a dispersed Na 2 WO 4 melt, under OCM conditions. The promotion of OCM is associated with the efficient interaction of the two phases in the gel-like formation, which apparently promotes the release of the catalytic active species. However, it is also shown that under reaction conditions the molten state of the Na 2 WO 4 promotes the growth of a separate Mn 2 O 3 phase, which enhances CO 2 formation over the OCM by reducing the effective level of interaction between the Mn and the W phases. As a whole, this work not only provides new data but also exemplifies a relatively simple and general tool for identifying catalyst descriptors that govern reaction performance.

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“…4 With the advancements in data science, chemical reaction data can be investigated with several approaches such as statistical analysis, 4 machine learning. 10,16,17 micro-kinetic simulations, 18 and meta-analysis. 19 Moreover, a recent experimental study demonstrated that the combination of high-throughput experiments and ML techniques can provide catalyst compositions with improved catalytic efficiencies for the OCM with less effort.…”

Section: Introductionmentioning

confidence: 99%

Reaction condition optimization for non-oxidative conversion of methane using artificial intelligence

Kim

Lee

et al. 2021

React. Chem. Eng.

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Catalyst screening for the oxidative coupling of methane: from isothermal to adiabatic operation via microkinetic simulations

Abstract: OCM catalysts underperforming in typical isothermal conditions could result in above average performances in adiabatically-relevant operating conditions.

Cited by 18 publications

References 54 publications

Gillespie-Driven kinetic Monte Carlo Algorithms to Model Events for Bulk or Solution (Bio)Chemical Systems Containing Elemental and Distributed Species

Gillespie-Driven kinetic Monte Carlo Algorithms to Model Events for Bulk or Solution (Bio)Chemical Systems Containing Elemental and Distributed Species

Correlating Properties of the Mn₂O₃–Na₂WO₄/SiO₂Catalyst with Statistically Estimated Parameters for the Oxidative Coupling of Methane

Reaction condition optimization for non-oxidative conversion of methane using artificial intelligence

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Catalyst screening for the oxidative coupling of methane: from isothermal to adiabatic operation via microkinetic simulations

Abstract: OCM catalysts underperforming in typical isothermal conditions could result in above average performances in adiabatically-relevant operating conditions.

Cited by 18 publications

References 54 publications

Gillespie-Driven kinetic Monte Carlo Algorithms to Model Events for Bulk or Solution (Bio)Chemical Systems Containing Elemental and Distributed Species

Gillespie-Driven kinetic Monte Carlo Algorithms to Model Events for Bulk or Solution (Bio)Chemical Systems Containing Elemental and Distributed Species

Correlating Properties of the Mn2O3–Na2WO4/SiO2Catalyst with Statistically Estimated Parameters for the Oxidative Coupling of Methane

Reaction condition optimization for non-oxidative conversion of methane using artificial intelligence

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Product

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Correlating Properties of the Mn₂O₃–Na₂WO₄/SiO₂Catalyst with Statistically Estimated Parameters for the Oxidative Coupling of Methane