title: New models for predicting thermophysical properties of ionic liquid mixtures.
abstract: Potential applications of ILs require the knowledge of the physicochemical properties of ionic liquid (IL) mixtures. In this work, a series of semi-empirical models were developed to predict the density, surface tension, heat capacity and thermal conductivity of IL mixtures. Each semi-empirical model only contains one new characteristic parameter, which can be determined using one experimental data point. In addition, as another effective tool, artificial neural network (ANN) models were also established. The two kinds of models were verified by a total of 2304 experimental data points for binary mixtures of ILs and molecular compounds. The overall average absolute deviations (AARDs) of both the semi-empirical and ANN models are less than 2%. Compared to previously reported models, these new semi-empirical models require fewer adjustable parameters and can be applied in a wider range of applications.
authors.affiliation: Beijing Key Laboratory of Ionic Liquids Clean Process, State Key Laboratory of Multiphase Complex Systems, Key Laboratory of Green Process and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China. xpzhang@ipe.ac.cn sjzhang@ipe.ac.cn.
authors.forename: Ying
authors.initials: Y
authors.lastname: Huang
authors.forename: Xiangping
authors.initials: X
authors.lastname: Zhang
authors.forename: Yongsheng
authors.initials: Y
authors.lastname: Zhao
authors.forename: Shaojuan
authors.initials: S
authors.lastname: Zeng
authors.forename: Haifeng
authors.initials: H
authors.lastname: Dong
authors.forename: Suojiang
authors.initials: S
authors.lastname: Zhang