Modeling of Thermal Cracking Furnaces Via Exergy Analysis Using Hybrid Artificial Neural Network–Genetic Algorithm

Abstract

In this study, we try to make an exergy analysis of an olefin cracking furnace more understandable by coupling it with the use of an artificial neural network–generic algorithm (ANN–GA) modeling. The presented method permits to provide an energy diagnosis of the process under a wide range of operating conditions. As a case study, one of the petrochemical complexes in Iran has been considered. The Petrosim process simulator software was used to obtain thermodynamic properties of the process streams and to perform exergy balances. The results are validated with industrial data obtained from the plant. The exergy destruction and exergetic efficiency for the main system components and the entire system were calculated. The simulation results reveal that the exergetic loss of the process increases with increasing steam ratio (SR) and decreases with coil outlet temperature (COT) and residence time (RT). The results show that the overall exergetic efficiency of the system is about 65%. The recorded and calculated data have been used as inputs for the neural network. The results show that ANN–GA is a highly effective method to optimize the performance of the neural networks, predicting the overall exergy efficiency. Comparing to phenomenological modeling based on the detailed knowledge of the furnace condition, the use of the introduced ANN–GA model saves significant amount of the time needed for the performance prediction of cracking furnaces.