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.

本文提出了一种无模型的深度强化学习（DRL）策略，并以人工神经网络（ANN）作为反应模拟环境，从而获得了实验生物反应器的分批补料控制策略。所提出的方法是通过使用最先进的机器学习工具来控制反应的基本尝试，而无需借助对反应系统的成熟的机械理解。此应用程序利用DRL系列成员之一的异步优势Actor-Critic（A3C）算法，该算法利用actor-critic算法和并行学习代理的异步学习来实现学习过程的稳定性和效率。最终的控制器在分批补料生物反应器中表现出强大的性能，因为可以对其进行调节以满足各种限制因素，包括营养限制和培养长度。给出了产生蓝藻藻蓝蛋白（C-PC）的生物反应器的结果。Plectonema sp。UTEX1541。实验验证显示，与以非优化方式补充相同总养分的对照组相比，对照组的产品收率提高了52.1％，C-PC浓度提高了20.1％。