Reinforcement learning for the optimization and online control of emulsion polymerization reactors: Particle morphology

Abstract

Online control of emulsion polymerization reactors is challenging due to the complex nature of the polymerization process, which causes difficulties not only for online measurement, but also for real-time prediction and optimization of future trajectories. In this work, the use of reinforcement learning (RL) to overcome these challenges is explored for the case of online control of particle morphology. It is shown in silico that assuming knowledge of the system dynamics in the form of a mathematical model, reinforcement learning can be used to train a neural network that can select optimal control actions in real-time to target a desired final polymer product. Moreover, it is shown that a second neural network model can be utilized as a state estimator to allow for full online control of particle morphology, which cannot be directly measured online. This work highlights the potential of machine learning for optimization and control in challenging polymerization systems.