Continuous control of a polymerization system with deep reinforcement learning. (March 2019)

• Record Type: Journal Article
• Authors: Ma, Yan; Zhu, Wenbo; Benton, Michael G.; Romagnoli, José
• Is Part Of: Journal of process control. Volume 75(2019)
• Page Start: 40
• Page End: 47
• Publication Date: 2019-03
• Subjects: Deep reinforcement learning -- Polymerization -- Process control; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2018.11.004
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Deep reinforcement learning is a versatile tool for control problems. A controller is developed based on DDPG framework for a polymerization reactor. The controller follows the MW trajectory by adjusting the monomer and initiator flow rates. The resulting MWD from the DRL controller matches with the target distribution. Abstract: Reinforcement learning is a branch of machine learning, where the machines gradually learn control behaviors via self-exploration of the environment. In this paper, we present a controller using deep reinforcement learning (DRL) with Deep Deterministic Policy Gradient (DDPG) for a non-linear semi-batch polymerization reaction. Several adaptations to apply DRL for chemical process control are addressed in this paper including the Markov state assumption, action boundaries and reward definition. This work illustrates that a DRL controller is capable of handling complicated control tasks for chemical processes with multiple inputs, non-linearity, large time delay and noise tolerance. The application of this AI-based framework, using DRL, is a promising direction in the field of chemical process control towards the goal of smart manufacturing.