첨단제조공정 연구

Our research group focuses on the digital transformation and autonomous operation of (bio)manufacturing processes through a model-based systems engineering approach. This approach integrates mathematical modeling, process data analysis, artificial intelligence (AI), and optimal design and control techniques. We primarily target the continuous manufacturing of biopharmaceuticals. By applying hybrid modeling strategies that combine the interpretability of first-principles models with the predictive power of data-driven models, we aim to quantitatively understand and predict the complex behavior of bioprocesses. These models are implemented as digital twins and deployed in automated production environments to enable smart operational strategies—such as real-time optimization, quality prediction, anomaly detection, and autonomous control. We utilize systems engineering methods—such as model predictive control (MPC), quality-by-design (QbD), and optimal experimental design—selectively tailored to the characteristics of each process. Through the integrated use of theory, experimentation, and computation, we strive to advance the field of intelligent biomanufacturing both academically and industrially.