Reinforcement learning meets bioprocess control through behavior cloning: Real-world deployment in an industrial photobioreactor

The complexity of living cells as production units creates major challenges for maintaining optimal bioprocess conditions, especially in open Photobioreactors (PBRs) exposed to fluctuating environments. To address this, we propose a Reinforcement Learning (RL) controller for pH regulation in open PBR systems. This work represents, to the best of our knowledge, the first industrial validation of an RL-based control strategy in a bioprocess. Our method begins with an offline training stage in which the RL agent learns from trajectories generated by a nominal Proportional–Integral–Derivative (PID) controller, without direct interaction with the real system. This is followed by deployment, where the RL agent acts and collects data during the day and is fine-tuned every night, enabling adaptation to evolving process dynamics and stronger rejection of fast, transient disturbances. This continual policy adaptation allows the offline-trained RL to effectively handle the inherent nonlinearities and external disturbances in open PBRs. Simulation studies highlight the advantages of our method: the Integral of Absolute Error (IAE) was reduced by 8% compared to PID control, 6% relative to a classical Model Predictive Controller (MPC), and 5% relative to a standard off-policy RL. Moreover, control effort decreased substantially—by 54% compared to PID, 11% compared to MPC, and 7% compared to the standard off-policy RL. Finally, an 8-day experimental validation under varying environmental conditions confirmed the robustness and reliability of the proposed approach. Overall, this work demonstrates the potential of RL-based methods for bioprocess control and paves the way for their broader application to other nonlinear, disturbance-prone systems.