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Control of A Polyol Process Using Reinforcement Learning

机译：使用加强学习控制多元醇过程

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Reinforcement learning is a branch of machine learning, where an agent gradually learns a control policy via a combination of exploration and interactions with a system. Recent successes of model-free reinforcement learning (RL) has attracted tremendous attention from the process control community. For instance, RL has been successfully applied in very complex control tasks (e.g., games such as chess or Go that contain large state spaces) and is shown to be robust to uncertainties. These findings indicate that there is a significant potential to leverage RL methods to improve the control of chemical processes. In this work, RL was applied to a detailed and accurate simulation of an industrial polyol process. To manufacture the desired product, the RL controller is required to achieve the target ending conditions determined by four key parameters; meanwhile, economic factors are also considered in this process, including batch reaction time and total feed amounts. The obtained results show a high consistency between RL and the current optimal operating conditions. Additionally, an improvement opportunity was identified by extending current control bounds of the manipulated variables. This work illustrates that RL is capable of handling complicated industrial systems, even under realistic operating constraints.
机译：加强学习是机器学习的分支，其中代理人通过与系统的探索和交互的组合逐渐学习控制策略。无模型加强学习（RL）的最近成功引起了过程控制社区的巨大关注。例如，RL已成功应用于非常复杂的控制任务（例如，诸如国际象棋或包含大状态空间的游戏），并且被证明是对不确定性的鲁棒性。这些发现表明，利用RL方法具有显着的潜力来改善化学过程的控制。在这项工作中，RL被应用于对工业多元醇过程的详细和准确模拟。为了制造所需的产品，需要RL控制器来实现由四个关键参数确定的目标结束条件;同时，在该过程中也考虑了经济因素，包括分批反应时间和总进料量。所得结果显示RL和当前最佳操作条件之间的高一致性。另外，通过扩展被操纵变量的电流控制界来识别改进机会。这项工作说明R1能够处理复杂的工业系统，即使在现实的操作约束下也是如此。

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