Adaptive system identification of industrial ethylene splitter: A comparison of subspace identification and artificial neural networks

The manuscript considers the problem of data-driven modeling of an ethylene splitter (from an industrial plant). The process presently operates with end composition controllers that does not work well during process transition. The objective of the present work is to investigate the use of different data-driven techniques such as subspace identification and neural network-based methods for the purpose of developing a dynamic data-driven model. …