Machine-learning-based simulation and fed-batch control of cyanobacterial-phycocyanin production in Plectonema by artificial neural network and deep reinforcement learning
شبیه سازی مبتنی بر یادگیری ماشین و کنترل دسته ای تولید cyanobacterial-فایکوسیانین در Plectonema توسط شبکه عصبی مصنوعی و یادگیری تقویت عمیق-2020
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 employ- ing 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 biore- actor that produces cyanobacterial-phycocyanin (C-PC) in Plectonema sp. UTEX 1541. Experimental valida- tions 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.
Keywords: Deep reinforcement learning | Artificial neural network | Fed-batch control | C-phycocyanin | Plectonema