Modeling and Simulation of an Industrial Ethylene Oxide (EO) Reactor Using Artificial Neural Networks (ANN)

In the present work, a one-dimensional heterogeneous model was used for dynamic simulation of an industrial fixed-bed catalytic ethylene oxide (EO) reactor in the presence of long-term catalyst deactivation. In order to determine the level of optimum ethylene dichloride (EDC), a multilayer perceptron (MLP) neural network was used. In addition, the effect of inlet gas velocity on EO mole fraction of gas and solid phases was investigated. The model validation was carried out by comparison of model results with corresponding industrial conditions and over a period of three operating years. A good agreement was found between the simulation results of the dynamic model and historical process data. The error of simulation was found to be less than 5%. The results of the artificial neural network (ANN) modeling showed that the maximum selectivity occurs in the range of 0.37-0.42 ppm of EDC. Also, it was observed that with decrease of gas velocity the difference between the EO mole fraction of gas phase and solid phase increases. This behavior