Modeling Sorption and Diffusion of Alkanes, Alkenes, and their Mixtures in Silicalite: From MD and GCMC Molecular Simulations to Artificial Neural Networks. Issue 3 (20th January 2021)

• Record Type: Journal Article
• Authors: Gurras, Arsenios; Gergidis, Leonidas N.
• Is Part Of: Advanced theory and simulations. Volume 4:Issue 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-20
• Subjects: alkane–alkene mixtures -- artificial neural networks -- grand canonical Monte Carlo simulations -- molecular dynamics -- molecular simulations -- silicalite -- zeolites; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202000210
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: Molecular dynamics (MD) in canonical (NVT) statistical ensemble and grand canonical‐Monte Carlo (GCMC) simulations along with artificial neural network (ANN) techniques are used for the study of diffusion and sorption characteristics of small alkanes, alkenes, and their mixtures in silicalite. In particular, sorption isotherms and self‐diffusion coefficients of alkanes (ethane to hexane), alkenes (ethene to hexene), and the respective alkane–alkene mixtures (consisting of the same number of carbon atoms) in silicalite are studied. The findings are directly compared with recent magic‐angle spinning pulsed field‐gradient nuclear magnetic resonance experimental diffusivity measurements and are in close agreement. Furthermore, new results are provided for the alkane–alkene systems. The sorption data from GCMC simulations, the self‐diffusivity calculations from the MD simulations along with available experimental data are used for the development of ANN predictive modeling procedures in order to give generic sorption and diffusion predictions for pure alkanes, alkenes in different input values of fugacity, temperature, and sorbate loadings at the minimum computational resources and time. Finally, structural characteristics for pure alkane, alkenes, and alkane–alkene mixtures when confined in the silicalite framework are computed revealing sorption domains and siting preferences. Abstract : Molecular dynamics in canonical statistical ensemble and grand canonical Monte Carlo simulations along with artificial neural networks are used to study the diffusion and sorption of small alkanes, alkenes, and their mixtures in silicalite. The findings are compared and are in agreement with recent magic‐angle spinning pulsed field‐gradient nuclear magnetic resonance diffusivity measurements and sorption simulation and experimental data.