Developing optimisation tools is a key target in supporting computer-aided process design as the complexity of the designed space grows beyond conventional unit operations. A process design problem can be formulated as a search of an optimal processing route in the thermodynamic state space, going from feedstock to products. This paper describes a design architecture that enables reinforcement learning agent to use trial-and-error to narrow its search to the most promising routes, rather than exhaustively enumerating solutions. In each iteration, the agent employs previously collected data to guide the search for new trajectories. This is successfully demonstrated in a hydrogen production process using both conventional and intensified process design principles. The agent outperformed standard nonlinear optimisation methods in competitive computational time. Limitations and future work are discussed.

开发优化工具是支持计算机辅助过程设计的主要目标，因为设计空间的复杂性已经超出了常规单元操作的范围。可以将过程设计问题表述为在热力学状态空间中搜索从原料到产品的最佳加工路线。本文介绍了一种可以增强学习代理的设计架构使用试错法将搜索范围缩小到最有希望的路线，而不是穷举列举解决方案。在每次迭代中，代理都会使用以前收集的数据来指导寻找新轨迹。在使用常规工艺和强化工艺设计原理的制氢工艺中已成功证明了这一点。在竞争性的计算时间内，Agent优于标准的非线性优化方法。局限性和未来的工作进行了讨论。