A neural network for predicting normal boiling point of pure refrigerants using molecular groups and a topological index

• The optimum network is found by combining ANN and GA.
• The absolute average deviation of the proposed model is 1.87%.
• The ANN model can distinguish the isomers of refrigerants and predict the normal boiling point based on the molecular structure.

An artificial neuron network based on genetic algorithm is presented to predict the normal boiling point (Tb) of refrigerants from 16 molecular groups and a topological index. The 16 molecular groups used in this paper can cover most refrigerants or working fluids in refrigeration, heat pump and organic Rankine cycle; the chosen topological index is able to distinguish all the refrigerant isomers. A total of 334 data points from previous experiments are used to create this network. The calculated results, which are based on a developed numerical method, show a good agreement with experimental data; the average absolute deviations for training, validation and test sets are 1.83%, 1.77%, 2.13%, respectively. A performance comparison between the developed numerical model and the other two existing models, namely QSPR approach and UNIFAC group contribution method, shows that the proposed model can predict Tb of refrigerants in a better accord with experimental data.