| Summary: | The call for the advancement in our ability to design cleaner technologies, as well as mitigate our ecological footprint, requires the investigation of new energy related systems. Fundamental knowledge of the thermodynamics and phase behavior of such systems is essential for their development and industrial application. Accurate thermophysical properties are required, as limited or inaccurate data may affect the design of processes resulting in a financial or product yield loss; thus, the ability to reliably predict the properties and phase behavior of energy relevant fluids is essential to the development of new and continual improvement of existing chemical and energy processes. Traditional theoretical approaches based on semi-empirical or empirical equations of state that do not reflect molecular-level structure and interactions, are typically heavily reliant on correlations from experimental data, which may limit their general applicability. Molecular-based equations of state that take into account molecular structure are an attractive alternative because they yield a more accurate and predictive approach by accounting for the intrinsic effects of the microscopic interactions between molecules that ultimately determine the thermodynamic properties of the fluid. This results in parameters that are typically transferrable to entire classes of molecules. The statistical associating fluid theory for potentials of variable range (SAFT-VR) is one such molecular-based approach that describes chain molecules formed from hard-core monomers that interact via square well potentials of variable attractive range. In this work, systems with significant environmental applications, including carbon dioxide, organic sulfur and fluorine molecules, fatty acid methyl esters, and nanoparticle systems, are studied with the SAFT-VR approach. These systems were specifically chosen, as their unique features (e.g., large molecules, association interactions, electrostatics) have historically made their thermodynamic modeling difficult.
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