Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry

Highlights：

• This paper proposes a theoretical framework for digital twin-based production optimization, which integrates industrial IoT data processing and machine learning approaches.

• This paper designs a practice loop of information exchange between the physical factory and a virtual digital twin model, as well as digital twin modeling process, system architecture, and model evaluation indices.

• This paper provides a concrete solution to time series data processing issues in the petrochemical industry, such as frequency alignment, time lag issues, and high demand for immediacy.

• The framework and approach proposed are practiced in the catalytic cracking unit of a petrochemical factory, and the results show the effectiveness of this approach for production optimization.