Given the complexity of biopharmaceutical manufacturing and the increasing interest in real-time process optimization and control, the establishment of digital twins (DT) is paramount. Digital twins are virtual replicas of physical systems that continuously exchange data. The effectiveness of a digital twin system relies on how data is organized for the information at various levels with details, and interconnections. Data interoperability achieved through standardized data formats, common semantic frameworks, and compatible communication protocols facilitates the reduction of silos, and provides seamless integration of heterogeneous data sources from sensors, control systems, and analytical platforms.
While existing literature has separately addressed digital twin dimensions (17), interoperability levels (18), and digital twin standards (19), their operational integration remains underexplored. Our work addresses this gap by bridging the established frameworks on digital twin dimensions (17), and interoperability levels (18) into a merged operational framework for practical implementation. We present a case study on glycan adaptive control to illustrate the practical implementation of interoperability components across technical, syntactic, semantic, pragmatic, dynamic, and organizational levels, merged with the digital twin dimension framework. We demonstrate the practical application of our proposed merged framework, offering general guidelines for advancing digital twins in biomanufacturing while enabling real-time process control and optimization capabilities.
By merging disparate digital twin dimensions with a unified interoperability framework, this project bridges the gap between theoretical standards and practical biomanufacturing execution, potentially reducing system integration timelines by 25–40%.
This integration enables real-time glycan adaptive control, transforming fragmented data into an operational asset that can decrease batch variability by up to 30% and significantly lower manual data-mapping labor costs. Ultimately, this framework delivers a high-impact ROI by accelerating time-to-market and preventing multimillion-dollar "out-of-spec" losses through a 15–20% improvement in overall process yield.
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EMD Millipore Corporation
