Estimation of the thermal conductivity λ(T,P) of ionic liquids using a neural network optimized with genetic algorithms

In this study, an artificial neural network was optimized using a genetic algorithm in order to estimate the thermal conductivity of ionic liquids at different temperatures and pressures. Experimental thermal conductivity data of 41 ionic liquids (400 experimental data points) in the range from 0.10 to 0.22 W m−1 K−1 were used to obtain the proposed method for the temperature range of 273-390 K and the pressure range of 100-20,000 kPa. In addition, the molecular mass M and structure of molecules, represented by the number of well-defined groups forming the molecule, were provided as input parameters in order to characterize the different molecules of ionic liquids. A heterogeneous set of ionic liquids includes cations such as imidazolium, ammonium, phosphonium, pyrrolidinium, and pyridinium. It also includes anions such as halides, sulfonates, tosylates, imides, borates, phosphates, acetates, and amino acids. The whole dataset was divided into a training set with 300 experimental data points and a prediction set with 100 experimental data points. Several architectures were studied, and the optimum weights for the network were determined. The results showed that the proposed method to estimate the thermal conductivity of ionic liquids at different temperatures and pressures presented a good accuracy with lower deviations such as AARD less than 0.91% and R2 of 0.9969 for the training set, and AARD less than 0.84% with R2 of 0.9963 for the prediction set.