Rational Selection of Novel Solvents for CO2 Capture by Applying a Molecular-Based Equation of State

Abstract

Finding efficient technologies for CO2 capture to mitigate climate change while securing energy is an area of global concern. Among them, there is an interest in finding novel solvents to overcome some of the limitations of aqueous amines. However, in order to find the optimal solvents, it is essential to know the influence of their molecular structure and interactions on their thermodynamics and transport properties, as a key step to select the best solvent for the selected application. In addition, experimental data is still very limited at the required conditions for process design, further hindering the implementation of these new solvents. The goal of this PhD Thesis to advance in the state of the art in these two directions by developing and applying a robust solvent screening tool routed in the soft-SAFT molecular-based equation of state, complemented with other modeling techniques, to assess novel solvents for CO2 capture. The tool has been applied to different emerging solvents, allowing ranking them based on key performance indicators, starting from understanding their behavior at a molecular scale. The examined solvents include novel aqueous amines, hybrid chemical + physical solvents, and task designed solvents such as ionic liquids and deep eutectic solvents. For this purpose, a database summarizing properties and performance of available solvents was first developed for the initial selection of attractive alternative solvents and emerging trends in formulation of hybrid solvents. This guided the new extension of soft-SAFT to explicitly model polar interactions, polar soft-SAFT. The new extension was systematically tested and applied to a wide array of systems and thermodynamic properties. The thermophysical properties and CO2 absorption capabilities of emerging solvents identified from the database were calculated using the soft-SAFT formalism and its new polar extension. Complementary predictions from the quantum chemistry-based model (COSMO-RS) were used to validate soft-SAFT predictions in some cases when experimental data were unavailable, while the performance of the examined solvents for CO2 capture was assessed through integrating soft-SAFT with simplified key performance indicators, such as cyclic capacity, and regeneration energy. In some cases, other screening criteria were included such as solvent viscosity, and environmental impact. Lastly, the modeling and screening framework was extended to the absorption of other acid gases such as H2S in amine-based solvents. Results presented in this thesis reinforce the value of using molecular-based equations of state, combined with other modeling tools, on the practical search for novel solvents for CO2 capture providing information at process conditions from their fundamental knowledge.

Date of Award	May 2021
Original language	American English