AI-based optimal control of fed-batch biopharmaceutical process leveraging deep reinforcement learning

Abstract

Optimal control of the fed-batch biopharmaceutical process remains an open research and industrial challenge. The fed-batch process is characterized by non-steady operation, partially observable states, and batch uncertainties. Recently, deep reinforcement learning (DRL) has emerged as a powerful tool for chemical process systems. However, directly applying DRL to the simulation-based biopharmaceutical process has been proven a failure due to the mismatch between reinforcement learning algorithms and simulation-based environments. To fill this research gap, we proposed a novel DRL-based control framework that innovatively incorporates human knowledge. A case study of open-source simulator (IndPenSim), is used to verify the effectiveness of the proposed framework. The DRL controller demonstrates advantages in batch yields, computational loads, and measurement requirements compared to the existing control methods. An improvement of 14 % average penicillin yield is achieved with only 0.01∼0.03 s online computation time per step, showing its potential in the control applications for fed-batch biopharmaceutical process.