Controlling Paracetamol Batch Crystallization in Ethanol by Reinforcement Learning

In this work, we proposed a controller based on reinforcement learning (RL) for the unseeded batch crystallization of paracetamol in ethanol. The controller aims to achieve the crystal mean volume size and the crystal mass at fve different targets by manipulating the temperature. We used the deep deterministic policy gradient (DDPG) algorithm to train the control agent. The performance of the RL controller was compared to an NMPC using a population balance model (PBM) as its internal model and tested for the fve different scenarios. The controllers were also tested taking into account a 5% disturbance in the concentration measurement. Both controllers were able to reach values of the controlled variables close to the targets even accounting for disturbances. However, the RL controller was able to calculate the control action much faster than the NMPC and imposed less temperature changes, which presents as better alternative for real control applications. Therefore, the RL controller presented as a more suitable approach than an NMPC using a PBM as its internal model for controlling the paracetamol batch crystallization process.