Digital twin for subcritical extraction driven by data-mechanism fusion

Abstract Addressing the key issue of low automation in the production process due to the complex associations involved in sub-critical extraction, this study focuses on sub-critical extraction as the subject matter. It integrates data models with mechanism models to construct a multidimensional digital twin model and conduct systematic research. By combining simulation methods with real-time experimental data, the study establishes multilevel machine characteristics of data, behavior, and mechanisms, and identifies data patterns. From the perspectives of the physical production line, virtual production line, and data transmission, it builds an interactive mapping platform for multi-source data and carries out corresponding application research.