Machine-learning-based simulation and fed-batch control of cyanobacterial-phycocyanin production in Plectonema by artificial neural network and deep reinforcement learning

Abstract

In this paper, a model-free deep reinforcement learning (DRL) strategy is presented with an artificial neural network (ANN) as reaction simulation environment, to obtain a fed-batch control strategy for an experimental bioreactor. The proposed method is a fundamental attempt to control reactions by employing state-of-the-art machine learning tools without the aid of well-established mechanistic understanding of the reaction system. This application utilizes the Asynchronous Advantage Actor-Critic (A3C) algorithm, a member of the DRL family, that takes advantage of actor-critic algorithm and asynchronous learning by parallel learning agents to achieve stability and efficiency of the learning process. The resulting controller demonstrates robust performance in the fed-batch bioreactor since it can be adjusted to meet varying constraining factors including nutrient limitations and culture lengths. Results are presented for a bioreactor that produces cyanobacterial-phycocyanin (C-PC) in Plectonema sp. UTEX 1541. Experimental validations show a 52.1% increase in the product yield, and a 20.1% increase in C-PC concentration compared to a control group with the same total nutrient input replenished in a non-optimized manner.