Mathematical and artificial neural network modeling of production of ethylene from ethane pyrolysis in a tubular reactor

Pyrolysis of lower alkanes is among the main industrial methods for the production of light olefins. In this paper, an artificial neural network (ANN) model was developed in order to predict production of ethylene from ethane pyrolysis based on data obtained from mathematical modeling of the process in a plug flow reactor. Results obtained from the mathematical models were validated using experimental data from the literature. According to the results, the linear regression between network outputs and corresponding targets are proven satisfactory with a correlation coefficient of 1. The optimum number of neurons of 10 was obtained at hidden layer.