Implementation of Artificial Neural Networks in the assessment of CO2 solubility in deep eutectic and ionic liquid solvents – Performance and cost comparison

• Published in: Sustainable Chemistry for Climate Action 2022, Vol.1, p.100007, Article 100007
• Main Authors: Sagar, Avikal, Upadhyayula, Sreedevi
• Format: Article
• Language: English
• Subjects: Absorption; CO2 capture; DES mixtures; Gas solubility; Ionic liquids; Neural networks
• ISSN: 2772-8269; 2772-8269
• DOI: 10.1016/j.scca.2022.100007

In order to counteract the economic and environmental issues presented by Ionic Liquids (ILs) for carbon capture in post-combustion processes, Deep eutectic solvents (DESs) are being researched as potential absorbents. These are an emerging class of ILs that have a strong contribution from hydrogen bonding and have shown promising trends in CO2 absorption in recent times. In this study, three hydrogen bond acceptors (HBA), along with 2-hydroxypropanoic acid (Lactic Acid (LA)) as the hydrogen bond donor (HBD), have been identified and analyzed as CO2 absorbents. Considering their structural properties, thermodynamic behavior, and experimental conditions as input parameters, a backpropagation neural network (BPNN) has been implemented to analyze and predict the extent of CO2 solubility within each of the DES mixtures. BPNN successfully predicted trends in the solubility as a function of the alkyl chain length, temperature, and pressure. It was observed that the solubility of CO2 increased with increasing alkyl chain length and pressure but decreased with increasing values of temperature. DES is found to be more economical than other ionic liquid solvents used for CO2 absorption. [Display omitted]