A reinforcement learning-based temperature control of fluidized bed reactor in gas-phase polyethylene process

Abstract

This study investigates using deep reinforcement learning (DRL) with proportional-integral-derivative (PID) control for temperature cascade control in a fluidized bed reactor within a commercial gas-phase polyethylene process. The heat exchange system's nonlinearity and frequent disturbances pose challenges for PID controllers, particularly under varying conditions. To address this, a PID-DRL cascade control scheme is developed, where a DRL controller is used in the secondary loop. The DRL controller, designed using the actor-critic framework, is trained using the Deep Deterministic Policy Gradient algorithm. The DRL controller is evaluated in three stand-alone secondary loop experiments, as well as three cascade control experiments. Results reveal the DRL controller surpass the traditional PID controller in both scenarios. The DRL controller shows better set point tracking and interference suppression, indicated by lower integral absolute error (IAE) values. The proposed cascade control structure can be used to enhance reactor stability and product quality in gas-phase polyethylene processes.