Leveraging Historical Process Data for Recombinant P. pastoris Fermentation Hybrid Deep Modeling and Model Predictive Control Development

Hybrid modeling techniques are increasingly important for improving predictive accuracy and control in biomanufacturing, particularly under data-limited conditions. This study focuses on the development and experimental validation of a hybrid deep learning model predictive control (MPC) framework for fed-batch P. pastoris fermentations producing recombinant proteins. Bayesian optimization and grid search were employed to identify optimal network architectures, revealing that models combining LSTM layers with fully connected layers provided the best balance between prediction accuracy and computational efficiency. The top-performing architecture was adapted to a new dataset involving bacteriophage Qβ coat protein production using transfer learning, yielding strong predictive performance with low validation and test losses. Experimental implementation of the novel hybrid MPC system demonstrated robust real-time control of substrate feeding to maintain target specific growth rates. While moderate discrepancies were observed in biomass and product predictions—particularly during the methanol adaptation phase and late-stage cytotoxic conditions—the controller effectively regulated process dynamics. These findings suggest that hybrid neural networks, when integrated with MPC and refined through automated architecture selection, offer a practical and generalizable solution for real-time control in microbial bioprocesses. This work provides a validated framework for deploying hybrid digital twins in fermentation and highlights the need to account for physiological effects in future models.