PVTxy properties of CO2 mixtures relevant for CO2 capture, transport and storage: Review of available experimental data and theoretical models

The knowledge about pressure–volume–temperature–composition (PVTxy) properties plays an important role in the design and operation of many processes involved in CO2 capture and storage (CCS) systems. A literature survey was conducted on both the available experimental data and the theoretical models associated with the thermodynamic properties of CO2 mixtures within the operation window of CCS. Some gaps were identified between available experimental data and requirements of the system design and operation. The major concerns are: for the vapour–liquid equilibrium, there are no data about CO2/COS and few data about the CO2/N2O4 mixture. For the volume property, there are no published experimental data for CO2/O2, CO2/CO, CO2/N2O4, CO2/COS and CO2/NH3 and the liquid volume of CO2/H2. The experimental data available for multi-component CO2 mixtures are also scarce. Many equations of state are available for thermodynamic calculations of CO2 mixtures. The cubic equations of state have the simplest structure and are capable of giving reasonable results for the PVTxy properties. More complex equations of state such as Lee–Kesler, SAFT and GERG typically give better results for the volume property, but not necessarily for the vapour–liquid equilibrium. None of the equations of state evaluated in the literature show any clear advantage in CCS applications for the calculation of all PVTxy properties. A reference equation of state for CCS should, thus, be a future goal.

► Accurate knowledge about the thermodynamic properties of CO2 is essential in the design and operation of CCS systems.
► Experimental data about the phase equilibrium and density of CO2-mixtures have been reviewed.
► Equations of state have been reviewed too regarding CO2-mixtures. None has shown any clear advantage in CCS applications.
► Identified knowledge gaps suggest to conducting more experiments and developing novel models.