Development of kinetic Monte Carlo and Bin-Monte Carlo schemes for simulation of mixtures – vapor–liquid equilibria &amp; adsorption

Nguyen, Van Thuong; Fan, Chunyan; Razak, Musab Abdul; Do, D.D.; Nicholson, D.; Ustinov, E. A.

doi:10.1016/j.ces.2013.07.041

Cited by 6 publications

(5 citation statements)

References 29 publications

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“…The general chosen s -parameters are used: s min = 0.36, s max = 0.72, s seg = 0.66. Left: argon–methane (122.89 K), GEMC simulation by Nguyen et al Right: methanol–MTBE (338.15 K), RGEMC simulation by Lísal et al…”

Section: Resultsmentioning

confidence: 99%

“…Simulations are more ab initio than any of the engineering models mentioned before. Several studies showed very accurate results; − however, other studies showed that these results highly depend on the used force field. − Moreover, with simulations requiring up to one billion Monte Carlo cycles for each composition, it is also a time-consuming process. To reduce the computational cost, Blanco et al , introduced a pair sampling method for a quick estimation of the Flory–Huggins interaction parameter.…”

Section: Introductionmentioning

confidence: 99%

“…h i b i t e d .avoid influence of the force field, we test the PAC-MAC model with molecular simulations using the same force field.Comparison with Molecular Simulations. In Figure10, we compare the results of PAC-MAC with the results of Gibbs ensemble Monte Carlo simulations for a mixture of argon− methane18 and a mixture of methanol−MTBE19 using the same force field. The PAC-MAC method only requires the sampling of 3m pair configurations (m = 10 5 ) between two molecules to obtain a full VLE diagram, whereas the Gibbs ensemble Monte Carlo simulations were performed by sampling 1.5−3 × 10 5 cycles consisting of 5514 steps in a box containing 512 particles for just a single liquid fraction at the VLE.…”

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confidence: 99%

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Force-Field Based Quasi-Chemical Method for Rapid Evaluation of Binary Phase Diagrams

Sweere

Fraaije

2015

J. Phys. Chem. B

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We present the Pair Configurations to Molecular Activity Coefficients (PAC-MAC) method. The method is based on the pair sampling technique of Blanco (Fan, C. F.; Olafson, B. D.; Blanco, M.; Hsu, S. L. Application of Molecular Simulation to Derive Phase Diagrams of Binary Mixtures. Macromolecules 1992, 25, 3667-3676) with an extension that takes the packing of the molecules into account by a free energy model. The intermolecular energy is calculated using classical force fields. PAC-MAC is able to predict activity coefficients and corresponding vapor-liquid equilibrium diagrams at least 4 orders of magnitude faster than molecular simulations. The accuracy of the PAC-MAC method is tested by comparing the results with experimental data and with the results of the COSMO-SAC model (Lin, S.-T.; Sandler, S. I. A Priori Phase Equilibrium Prediction from a Segment Contribution Solvation Model. Ind. Eng. Chem. Res. 2002, 41, 899-913). PAC-MAC (using the OPLS-aa force field) is shown to be comparable in accuracy to COSMO-SAC, at the considerable advantage that PAC-MAC in principle does not require quantum calculation, provided proper force fields to be available.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Force-Field Based Quasi-Chemical Method for Rapid Evaluation of Binary Phase Diagrams

Sweere

Fraaije

2015

J. Phys. Chem. B

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“…Ab initio (molecular dynamics) methods simulate the behavior of molecules in quantum space, producing results valid within very small volumes (≤100 Å), and are frequently dependent on the chosen model chemistries used . Monte Carlo calculations have also been successfully applied to variants of ab initio methods, producing good predictions of LVE equilibrium for pure and binary mixtures at different ranges of pressures and temperatures. − The latter two approaches were not considered in the context of this work.…”

Section: Designing Surrogate Mixtures For Pyrolysis Condensatesmentioning

confidence: 99%

Modeling of Liquid–Vapor Phase Equilibria of Pyrolysis Bio-oils: A Review

Fonseca,

Funke

2024

Ind. Eng. Chem. Res.

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The intricate composition of pyrolysis bio-oil necessitates the development of reliable phase equilibrium models, with a specific focus on liquid−vapor equilibrium pertinent to both condensation design and distillation, especially when utilizing nonwood feedstocks. Four key challenges intrinsic to pyrolysis bio-oil are scrutinized: the selection of an appropriate phase equilibrium model, formulation of a suitable surrogate mixture, representation of the elusive high-molecular-weight residue fraction, and the estimation of absent thermophysical properties. Despite a discernible inclination toward activity coefficient models, the literature reveals a diverse array of phase equilibrium models, pointing to the indispensability of considering a nonideal liquid phase and raising concerns about the reliability of certain models in the absence of comprehensive experimental data. The judicious choice of a surrogate mixture emerges as pivotal, given the prevalence of unknown components and the dearth of precise compound-specific data, foreseeing a future where diverse surrogate mixtures coexist. Existing surrogate mixtures are reviewed and recommendations given to guide effective design of such mixtures. The absence of thermophysical properties for pyrolysis bio-oil compounds prompts the use of estimation methods, introducing a challenge in achieving comparable reliability to experimental values. Quantitative comparison of estimation performance shows no distinct trend favoring a singular estimation method, a composite of approaches is suggested to enhance overall model precision. To propel the field forward, a critical need is identified for augmented availability of reliable experimental phase equilibrium data for both pyrolysis bio-oil and its constituent compounds, coupled with their thermophysical properties, to establish a robust foundation for the widespread and efficient application of pyrolysis bio-oil across diverse industries.

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“…[18] Recently, a canonical kinetic Monte Carlo (C-kMC) method has been developed as an alternative to conventional MC and has been shown to be successful in describing vapour-liquid equilibria (VLE) and vapour-solid equilibria (VSE) of simple gases [18,19] and their mixtures. [20] A unique feature of this method is the rejection-free movement of particles, which enables both efficient scanning of the energy space and the calculation of the chemical potential within the course of a single simulation and without resorting to time-consuming supplementary procedures such as Widom insertion.…”

Section: Introductionmentioning

confidence: 99%

Application of kinetic Monte Carlo method to the vapour–liquid equilibria of associating fluids and their mixtures

Nguyen

Tan

et al. 2015

Molecular Simulation

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Canonical kinetic Monte Carlo (C-kMC) simulations have been carried out to assess their feasibility and potential for calculating the vapour-liquid equilibria of various pure components with increasingly strong electrostatic interactions (carbon dioxide, methanol, ammonia and water) over a wide range of temperatures and for methanol/water mixtures at 298 K. The simulation results show that C-kMC is successful as a method for studying phase equilibria and thermodynamic properties. For all the examples investigated, the performance of the C-kMC method is at least as good as that of the conventional Monte Carlo (MC) methods and is efficient at low temperature where these fail. It also provides a route that is superior to the Widom method for the calculation of chemical potential. We recommend this method for this purpose and as an alternative to conventional MC for simulations of strongly associating fluids and at low temperatures.

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Development of kinetic Monte Carlo and Bin-Monte Carlo schemes for simulation of mixtures – vapor–liquid equilibria & adsorption

Cited by 6 publications

References 29 publications

Force-Field Based Quasi-Chemical Method for Rapid Evaluation of Binary Phase Diagrams

Force-Field Based Quasi-Chemical Method for Rapid Evaluation of Binary Phase Diagrams

Modeling of Liquid–Vapor Phase Equilibria of Pyrolysis Bio-oils: A Review

Application of kinetic Monte Carlo method to the vapour–liquid equilibria of associating fluids and their mixtures

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