| Title |
Leveraging historical process data for recombinant P. pastoris fermentation hybrid deep modeling and model predictive control development |
| Authors |
Bolmanis, Emils ; Galvanauskas, Vytautas ; Grigs, Oskars ; Vanags, Juris ; Kazaks, Andris |
| DOI |
10.3390/fermentation11070411 |
| Full Text |
|
| Is Part of |
Fermentation.. Basel : MDPI. 2025, vol. 11, iss. 7, art. no. 411, p. 1-22.. ISSN 2311-5637 |
| Keywords [eng] |
Pichia pastoris ; hybrid process model ; deep learning ; Bayesian optimization ; hybrid model architecture screening ; transfer learning ; model predictive control ; hybrid MPC |
| Abstract [eng] |
Hybrid modeling techniques are increasingly important for improving predictive accuracy and control in biomanufacturing, particularly in data-limited conditions. This study develops and experimentally validates a hybrid deep learning model predictive control (MPC) framework for recombinant P. pastoris fed-batch fermentations. Bayesian optimization and grid search techniques were employed to identify the best-performing hybrid model architecture: an LSTM layer with 2 hidden units followed by a fully connected layer with 8 nodes and ReLU activation. This design balanced accuracy (NRMSE 4.93%) and computational efficiency (AICc 998). This architecture was adapted to a new, smaller dataset of bacteriophage Qβ coat protein production using transfer learning, yielding strong predictive performance with low validation (3.53%) and test (5.61%) losses. Finally, the hybrid model was integrated into a novel MPC system and experimentally validated, demonstrating robust real-time substrate feed control in a way that allows it to maintain specific target growth rates. The system achieved predictive accuracies of 6.51% for biomass and 14.65% for product estimation, with an average tracking error of 10.64%. In summary, this work establishes a robust, adaptable, and efficient hybrid modeling framework for MPC in P. pastoris bioprocesses. By integrating automated architecture searching, transfer learning, and MPC, the approach offers a practical and generalizable solution for real-time control and supports scalable digital twin deployment in industrial biotechnology. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2025 |
| CC license |
|
