Predicting the Viscosity of Ionic Liquids by the ELM Intelligence Algorithm

Abstract

Predicting the viscosity of ionic liquids (ILs) is crucial for their applications in chemical and related industries. In this study, a large data set of experimental viscosity data of ILs with a wide range of viscosity (7.83–142 000 cP), pressure (1–3000 bar), and temperature (258.15–395.32 K) are employed to build predictive models. The structures of cations and anions for 89 ILs are optimized, and the Sσ-profiles descriptors are calculated using the quantum chemistry method.Two new models are developed by using extreme learning machine (ELM) intelligence algorithm with the temperature, pressure, and a number of Sσ-profiles descriptors as input parameters. The coefficient of determination (R2) and average absolute relative deviation (AARD %) of the total sets of the two predictive models are 0.982, 2.21% and 0.951, 4.10%, respectively. The results show that the two ELM models are reliable for predicting the viscosity of ILs.