Prediction of programmed-temperature retention values of naphthas by wavelet neural networks

The wavelet neural network (WNN) was used to predict the programmed-temperature retention values of naphthas. In WNN, a Morlet mother wavelet was used as a transfer function, and the convergence speed was faster than other neural networks. Sixty-four compounds (selected randomly from 94) were used as a training set, and the 30 remaining compounds were used as a test set. A very satisfactory result was obtained only after about 8000 training epochs. The other two methods, the artificial neural network (ANN) and the Simpson integral method, were also used for this study. The comparison of results obtained from three methods showed that the WNN is the most suitable tool in predicting programmed-temperature retention values of naphthas, consequently this method can be used to provide reliable data for the petrochemical industry.