Molecular Simulation of Reaction and Adsorption in Nanochemical Devices: Increase of Reaction Conversion by Separation of a Product from the Reaction Mixture

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Abstract

We present a novel simulation tool to study fluid mixtures that are simultaneously chemically reacting and adsorbing within a molecularly porous material. The method is a combination of the Reaction Ensemble Monte Carlo method and the Dual Control Volume Grand Canonical Molecular Dynamics technique. The method, termed the Dual Control Cell Reaction Ensemble Molecular Dynamics (DCCRxMD) method, allows for the calculation of both equilibrium and nonequilibrium transport properties in porous materials, such as diffusion coefficients, permeability and mass flux. Simulation control cells, which are in direct physical contact with the porous solid, are used to maintain the desired reaction and flow conditions for the system. The simulation setup closely mimics an actual experimental system in which the thermodynamic and flow parameters are precisely controlled. We present an application of the method to the dry reforming of methane within a nanoscale reactor in the presence of a semipermeable nanomembrane modelling silicalite. We studied the effects of the nanomembrane structure and porosity on the reaction species permeability by considering three different nanomembrane models. We also studied the effects of an imposed pressure gradient across the nanomembrane on the mass flux of the reaction species. Conversion of syngas (H2/CO) increased significantly in all the nanoscale membrane reactor systems considered. The results of this work demonstrate that the DCC-RxMD method is an attractive computational tool in the design of nanoscale~membrane reactors for industrial processes. © Springer-Verlag 2004.

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Smith, W. R., & Li’Sal, M. (2004). Molecular Simulation of Reaction and Adsorption in Nanochemical Devices: Increase of Reaction Conversion by Separation of a Product from the Reaction Mixture. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3044, 392–401. https://doi.org/10.1007/978-3-540-24709-8_42

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