The Use of ANN and the Mathematical Model for Prediction of the Main Product Yields in the Thermal Cracking of Naphtha

Abstract

Thermal cracking of naphtha has such numerous reaction routes that the detailed reaction mechanism has not yet been determined. In this regard, a model of artificial neural networks (ANNs), using back propagation (BP), is developed for modeling thermal cracking of naphtha. The optimum structure of the neural network was determined by a trial-and-error method. Different structures were tried with several neurons in the hidden layer. The model investigates the influence of the coil outlet temperature, the pressure of the reactor, the steam ratio (H2O/naphtha), and the residence time on the pyrolysis product yields. A good agreement was found between model results and experimental data. A comparison between the results of the mathematical model and the designed ANN was also conducted and the relative absolute error was calculated. Performance of the ANN model was better than the mathematical model.