Digital Twin Model Development for Chemical Plants Using Multiple Time-Steps Prediction Data-Driven Model and Rolling Training

Abstract Data-driven operation monitoring and optimization of chemical plants can be performed using Digital Twin, along with intelligent algorithms. This study proposed a sequence-to-sequence rolling training algorithm to overcome the challenge of rolling predictions. The data were generated through dynamic simulation of the vapor-recompression C3 process using Aspen Plus. Studies showed that StS with rolling training could better fit the real data than the StS model. Moreover, StS with rolling training was able to present efficient long-term predictions as Digital Twin.