How to support decision making with reinforcement learning in hierarchical chemical process control?

Abstract

• Reinforcement learning (RL) in hierarchical levels of chemical process control. • Grouping RL methods at the hierarchical chemical control levels. • Comparison of traditional and RL controls in chemical process control. • Multi-agent reinforcement learning structures in chemical process control. • Development of CRISP-RL method, as the life cycle of a reinforcement learning project. In this review article, we explore the application of reinforcement learning (RL) at the different levels of hierarchical chemical process control, where reinforcement learning can improve efficiency and robustness in chemical process operations. RL algorithms are an optimal method for sequential decision making, therefore in chemical process control, where taking decisions is required continuously, RL can be a perfect fit due to its ability to handle dynamic, nonlinear, and uncertain environments. Reinforcement learning has already shown great potential in solving complex tasks, making it a promising approach for the challenges of chemical process control. We investigate the potential of reinforcement learning compared to traditional control methods. We present advanced multi-agent structures of RL, which can tackle large- scale chemical processes beyond the capabilities of a single agent. We introduce CRISP-RL (CRoss-Industry Standard Process for the development of Reinforcement Learning application), which is a paradigm that aims to deploy and maintain reinforcement learning projects, providing a methodology to handle and solve complex RL tasks and describe the current challenges and future directions for the integration of reinforcement learning into chemical process control.

Topics & Concepts