Integrating Computational Tools to Address the Thermophysical Behavior of Deep Eutectic Solvents in Gas Separation Applications

Identificador:  TDX:4489

Handle: https://hdl.handle.net/20.500.11797/TDX4489

Autors:  Tavares Duarte de Alencar, Luan Vittor

Resum:
Global warming stands as one of the most critical issues in modern science, profoundly impacting society and prompting legislative actions, regulations, and scientific studies. The primary cause is the anthropogenic emission of greenhouse gases (GHGs), which have increased significantly in recent years. Effective capture and separation techniques for GHGs reduction, such as solvent-based absorption, are pivotal for sustainability efforts. In this sense, Deep Eutectic Solvents (DESs) have emerged as a promising eco-friendly solution for gas capture due to their high absorption capacity, cost-effectiveness, non-toxicity nature, and biodegradability, presenting a sustainable alternative to conventional solvents. Understanding the thermophysical properties of DESs is crucial for their industrial application. Given the diversity of DESs combinations and varying industrial conditions, relying solely on experimental measurements is impractical. Therefore, developing computational models to predict these properties and guide experiments is crucial. When constructing predictive models, it is important to consider the influence of cosolvents like water on DESs properties, particularly viscosity. Thus, this thesis aims to incorporate diverse theoretical frameworks to elucidate the thermophysical characteristics of DESs and their mixtures with cosolvents. Moreover, it seeks to explore their application in GHGs capture, including within commercial high global warming potential refrigerant gas blends, as well as their role in capturing carbon dioxide (CO2) and separating it from ammonia (NH3). The proposed framework employs the soft-SAFT equation of state for developing an accurate and transferable model, alongside machine learning techniques artificial neural networks trained with molecular descriptors derived from atomic-level analyzes using COSMO-SAC. This comprehensive approach facilitates the screening of DESs thermophysical properties, providing insights into their potential as alternative absorbents for GHGs separation and capture.