Integration of reinforcement learning and model predictive control to optimize semi‐batch bioreactor

Abstract As the digital transformation of the bioprocess is progressing, several studies propose to apply data‐based methods to obtain a substrate feeding strategy that minimizes the operating cost of a semi‐batch bioreactor. However, the negligent application of model‐free reinforcement learning (RL) has a high chance to fail on improving the existing control policy because the available amount of data is limited. In this article, we propose an integrated algorithm of double‐deep Q‐network and model predictive control. The proposed method learns the action‐value function in an off‐policy fashion and solves the model‐based optimal control problem where the terminal cost is assigned by the action‐value function. For simulation study, the proposed method, model‐based method, and model‐free methods are applied to the industrial scale penicillin process. The results show that the proposed method outperforms other methods, and it can learn with fewer data than model‐free RL algorithms.