A genetic neural network was presented to predict the critical temperature of refrigerants. The inputs of the network included molecular groups and a topological index

and the output was the critical temperature. 16 molecular groups divided can cover most of the refrigerants or working fluids in refrigeration

heat pump and Organic Rankine Cycle research. The chosen topological index was able to distinguish all refrigerant isomers. The critical temperatures of refrigerants were estimated by the neural network after obtaining the optimized network structure and initial parameters by genetic algorithm. At the same time

in order to improve network generalization ability of prediction

200 data points were divided into three data sets including the training

validation

and test sets. The calculated results based on the developed network showed a good agreement with experimental data. The network can distinguish all refrigerant isomers and compared with the experimental data. The average absolute relative deviations for training

validation and test sets were 1.18%

1.69% and 1.28%

respectively.