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Simultaneous Process Design and Control Optimization using Reinforcement Learning

机译：使用加固学习的同步工艺设计和控制优化

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摘要

The performance of a chemical plant is highly affected by its design and control. A design cannot be accurately evaluated without its controls and vice versa. To optimally address design and control simultaneously, one must formulate a bi-level mixed-integer nonlinear program with a dynamic optimization problem as the inner problem; this is intractable. However, by computing an optimal policy using reinforcement learning, a controller with a closed-form expression can be computed and embedded into the mathematical program. In this work, an approach that uses a policy gradient method to compute the optimal policy, which is then embedded into the mathematical program is proposed. The approach is tested in a tank design case study and the performance of the controller is evaluated. It is shown that the proposed approach outperforms current state-of-the-art control strategies. This opens a whole new range of possibilities to address the simultaneous design and control of engineering systems.
机译：化工厂的性能受其设计和控制的影响很大。在没有其控制的情况下无法准确评估设计，反之亦然。为了同时最佳地寻址设计和控制，必须使用动态优化问题作为内部问题，必须配制一个双级混合整数非线性程序;这是棘手的。然而，通过计算使用加强学习的最佳政策，可以计算具有闭合表达式的控制器并将其嵌入到数学程序中。在这项工作中，提出了一种使用策略渐变方法来计算最佳策略的方法，然后嵌入到数学程序中。该方法在坦克设计案例研究中进行测试，并评估控制器的性能。结果表明，所提出的方法优于最新的最先进的控制策略。这为解决了工程系统的同时设计和控制来开辟了全新的可能性。

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