Sayee Prasaad Balaji

A Comparison of Advanced Monte Carlo Methods for Open Systems: CFCMC vs CBMC.

Two state-of-the-art simulation methods for computing adsorption properties in porous materials like zeolites and metal-organic frameworks are compared: the configurational bias Monte Carlo (CBMC) method and the recently proposed continuous fractional component Monte Carlo (CFCMC) method. We show that CFCMC is a very significant improvement over CBMC, and we can recommend it as our preferred method of choice. It is faster, more reliable, and (in contrast to CBMC) provides insight on whether or not the results are properly equilibrated. We also show that it is possible to combine the methods in a new CB/CFCMC hybrid method and derive the acceptance rules. This method achieves even higher insertion acceptance ratios.

Simulating the Reactions of CO2 in Aqueous Monoethanolamine Solution by Reaction Ensemble Monte Carlo Using the Continuous Fractional Component Method

Molecular simulations were used to compute the equilibrium concentrations of the different species in CO2/monoethanolamine solutions for different CO2 loadings. Simulations were performed in the Reaction Ensemble using the continuous fractional component Monte Carlo method at temperatures of 293, 333, and 353 K. The resulting computed equilibrium concentrations are in excellent agreement with experimental data. The effect of different reaction pathways was investigated. For a complete understanding of the equilibrium speciation, it is essential to take all elementary reactions into account because considering only the overall reaction of CO2 with MEA is insufficient. The effects of electrostatics and intermolecular van der Waals interactions were also studied, clearly showing that solvation of reactants and products is essential for the reaction. The Reaction Ensemble Monte Carlo using the continuous fractional component method opens the possibility of investigating the effects of the solvent on CO2 chemisorption by eliminating the need to study different reaction pathways and concentrate only on the thermodynamics of the system.

Solubilities of CO 2 , CH 4 , C 2 H 6 , and SO 2 in ionic liquids and Selexol from Monte Carlo simulations

Monte Carlo simulations are used to calculate the solubility of natural gas components in ionic liquids (ILs) and Selexol, which is a mixture of poly(ethylene glycol) dimethyl ethers. The solubility of the pure gases carbon dioxide (CO2), methane (CH4), ethane (C2H6), and sulfur dioxide (SO2) in the ILs 1-alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([Cnmim][Tf2N], n = 4, 6), 1-ethyl-3-methylimidazolium diethylphosphate ([emim][dep]), and Selexol (CH3O[CH2CH2O]nCH3, n = 4, 6) have been computed at 313.15 K and several pressures. The gas solubility trend observed in the experiments and simulations is: SO2 > CO2 > C2H6 > CH4. Overall, the Monte Carlo simulation results are in quantitative agreement with existing experimental data. Molecular simulation is an excellent tool to predict gas solubilities in solvents and may be used as a screening tool to navigate through the large number of theoretically possible ILs.

A direct method for calculating thermodynamic factors for liquid mixtures using the Permuted Widom test particle insertion method

Understanding mass transport in liquids by mutual diffusion is an important topic for many applications in chemical engineering. The reason for this is that diffusion is often the rate limiting step in chemical reactors and separators. In multicomponent liquid mixtures, transport diffusion can be described by both generalized Ficks law and the Maxwell–Stefan theory. The Maxwell–Stefan and Fick approaches in an n-component system are related by the so-called thermodynamic factor [R. Taylor and H.A. Kooijman, Chem. Eng. Commun, 102, 87 (1991)]. As Fick diffusivities can be measured in experiments and Maxwell–Stefan diffusivities can be obtained from molecular simulations/theory, the thermodynamic factors bridge the gap between experiments and molecular simulations/theory. It is therefore desirable to be able to compute thermodynamic factors from molecular simulations. Unfortunately, presently used simulation techniques for computing thermodynamic factors are inefficient and often require numerical differentiation of simulation results. In this work, we propose a modified version of the Widom test-particle method to compute thermodynamic factors from a single simulation. This method is found to be more efficient than the conventional Widom test particle insertion method combined with numerical differentiation of simulation results. The approach is tested for binary systems consisting of Lennard-Jones particles. The thermodynamic factors computed from the simulation and from numerically differentiating the activity coefficients obtained from the conventional Widom test particle insertion method are in excellent agreement.