Thermodynamic Study of Binary Systems Containing Carbon Dioxide and Associated Gases Using Molecular Simulation Techniques

The reduction of greenhouse gas (GHG) emissions has become an utmost objective for many industrialized countries. Industrial activities involving the combustion of fossil resources are responsible for a large part of the human induced carbon dioxide (CO2) emissions. The carbon dioxide capture and storage (CCS) has been already proven technically feasible and more recently the carbon dioxide capture and utilization (CCU) economically viable, to regulate CO2 emissions to the atmosphere. Depending on its origin and despite performed treatments, the injected CO2 may contain small amounts of associated gaseous components such as O2, N2, SOx, NOx, as well as traces of noble gases. The experimental knowledge of the thermodynamic behavior for such gas mixtures or even for some binary gas mixtures is often scarce. Molecular simulation techniques have been proven as cost-efficient alternatives to experimental measurements, especially when hazardous compounds and/or extreme pressure or temperature conditions are considered. These last years, we have devoted much effort to the use of molecular simulation techniques as well as to the development of accurate atomistic representations for gas molecules, to predict equilibrium and transport properties (phase envelopes, densities, viscosities, critical coordinates…) for mixtures containing CO2 and associated gaseous components, for conditions encountered in CCS and CCU stages. In this communication, we propose a compendium of data we previously obtained using Monte Carlo (MC) and Molecular Dynamics (MD) simulation techniques. Indeed, we have shown that molecular simulation tools used together with accurate force fields are efficient to predict equilibrium and transport properties for mixtures containing CO2 and associated gaseous components, and allow extrapolations to severe pressure or temperature conditions. This work illustrates how molecular simulations may be used as a tool to mitigate the lack of experimental data.