Prediction of ionic liquid thermophysical properties by group contribution method using random forest regression

• Main Author: Nuqui, Jesus Patrick E.
• Format: text
• Language: English
• Published: Animo Repository 2023
• Subjects: Materials—Thermal properties; Materials—Thermal conductivity; Chemical Engineering; Engineering; Thermodynamics
• Online Access: https://animorepository.dlsu.edu.ph/etdm_chemeng/20; https://animorepository.dlsu.edu.ph/context/etdm_chemeng/article/1020/viewcontent/Prediction_of_ionic_liquid_thermophysical_properties_by_group_contribution_method_using_random_forest_regression.pdf
• Institution: De La Salle University

Ionic liquids are tunable, there are vast combinations of cations and anions and attached substituent groups that can be combined together to achieve a target property. Mathematical equations in which the contributions of each component groups of an ionic liquid are considered in a predictive model...