Ethane Steam Cracking Inferential Model Development using Artificial Neural Network

Abstract

Abstract During normal operation, the product yield distribution of ethane steam cracking is obtained using analyzers and lab sampling to measure the cracking severity. Since analyzers take time to produce result, primarily depending on analyzers and lab testing to determine main product yield will hinder immediate control action to the process. In this study, data generated from ASPEN Plus Dynamics is used as feed data to develop the inferential model for product yield prediction based on real time operating parameters. The finalized model is a 4x2 ANN model, with 10 hidden nodes. Mean Squared Error (MSE) analysis shows that the model can accurately predict ethane and ethylene yields of the process, with MSE of 2.78x10-5 and 3.41x10-6, respectively. On top of that, R-squared analysis shows goodness-of-fit of 99.4% and 98.4%, respectively.