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.

本研究研究了使用深度强化学习 (DRL) 和比例积分微分 (PID) 控制来控制商业气相聚乙烯工艺中流化床反应器的温度串级控制。热交换系统的非线性和频繁的扰动给 PID 控制器带来了挑战，特别是在变化的条件下。为了解决这个问题，开发了 PID-DRL 串级控制方案，其中在二次回路中使用 DRL 控制器。DRL 控制器使用 actor-critic 框架设计，并使用深度确定性策略梯度算法进行训练。DRL 控制器在三个独立的二次回路实验以及三个级联控制实验中进行评估。结果表明，DRL 控制器在两种情况下都优于传统的 PID 控制器。DRL 控制器表现出更好的设定点跟踪和干扰抑制，这由较低的积分绝对误差 (IAE) 值表示。所提出的级联控制结构可用于提高气相聚乙烯工艺中的反应器稳定性和产品质量。