Prediction of solubility of gases in polystyrene by Adaptive Neuro-Fuzzy Inference System and Radial Basis Function Neural Network
Adaptive Neuro-Fuzzy Inference System (ANFIS) and Radial Basis Function Neural Network (RBF NN) have been developed for prediction of solubility of various gases in polystyrene. Solubility of butane, isobutene, carbon dioxide, 1,1,1,2-tetrafluoroethane (HFC-134a), 1-chloro-1,1-difluoroethane (HCFC-142b), 1,1-difluoroethane (HFC-l52a) and nitrogen in polystyrene is modeled by ANFIS and RBF NN in a wide range of pressure and temperature with high accuracy. The results obtained in this work indicate that ANFIS and RBF NN are effective methods for prediction of solubility of gases in polystyrene and have better accuracy and simplicity compared with the classical methods.
ann artificial neural network anfis adaptive neuro-fuzzy inference system hfc-134a 1 1 2-tetrafluoroethane hcfc-142b 1-chloro-1 1-difluoroethane hfc-l52a 1 mlp multi-layer perceptron bp back-propagation ps polystyrene ard average relative deviation sl eos sanchezlacombe equation of state rbf nn radial basis function neural network