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. To this end, first an ethylene splitter simulation model is built that replicates industrial operation. The ability of the simulation model to capture the key traits of the process dynamics are first established by comparing it with data from the plant operation. The simulation model is subsequently utilized to work as a test bed for future control purposes and to serve as an additional test of the modeling approaches. An online model adaptation scheme is developed to improve the model's prediction capabilities under new operation patterns.

该手稿考虑了乙烯分离器（来自工厂）的数据驱动建模问题。当前，该过程使用最终组成控制器运行，该过程在过程过渡期间无法正常运行。本工作的目的是研究不同数据驱动技术的使用，例如子空间识别和基于神经网络的方法，以开发动态数据驱动模型。为此，首先要建立一个模拟工业生产的乙烯分离器仿真模型。首先通过将仿真模型与工厂运营数据进行比较，来建立捕获过程动力学关键特征的仿真模型的能力。随后将仿真模型用作测试床，以用于将来的控制目的，并用作建模方法的附加测试。开发了一种在线模型自适应方案，以提高新操作模式下模型的预测能力。