Prediction of heat capacities of ionic liquids using chemical structure based networks

Ionic liquids (ILs) have various desired properties which bring them as useful and applicable compounds in different industrial processes. Heat capacity of ILs is one of their main properties which is required in various engineering and design applications. Hence, developing accurate and general models for prediction of this property is important. In this communication, two accurate and general models based on Radial Basis Function Neural Network (RBF-NN) and Multilayer Perceptron Neural Network (MLP-NN) were developed for estimation of heat capacities of ILs. The input parameters of the models are temperature, molecular weight of IL and several structural related parameters for each IL. The models were developed based on 2940 experimental data for 56 ILs. The reliability and accuracy of predictions of the developed models were examined by using statistical and graphical methods as well as comparing the results of the models with outcomes of recently developed literature correlations. Results show that the developed models are accurate and reliable and are superior to literature correlations for predictions of heat capacity of ILs. The average absolute relative deviation of RBF-NN and MLP-NN models for predicted heat capacity data was 0.83% and 1.04% respectively.