Dragonfly-Support Vector Machine for Regression Modeling of the Activity Coefficient at Infinite Dilution of Solutes in Imidazolium Ionic Liquids Using σ‑Profile Descriptors

Ionic liquids (ILs) have shown remarkable
potential for applications
in separation, such as extractive distillation and liquid–liquid
extraction. Crucial to these applications is the estimation of a significant
property of the ILs which is the infinite dilution activity coefficient
(IDAC) of different solutes in ILs. In this context, the present paper
aims to model IDAC of 17 solutes in 44 imidazolium ILs using 2666
experimental data points gathered from the literature and based on
support vector machine for the regression (SVMr) learning algorithm.
Two models are developed, one based on SVMr and the other one based
on dragonfly algorithm (DA) associated with SVMr. Both models consider
the same set of predictive variables which are the temperature, the
molecular weight of solute and solvent, and five conductor-like screening
models for real solvents (COSMO-RS) σ-profile descriptors related
to the solute and IL. The DA is applied for optimization of SVMr hyper-parameters.
The results show the superiority of the DA-SVMr model demonstrated
by its correlation coefficient (R) and root mean
square error values of 0.996 and 0.170, respectively.