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基于深度强化学习的自优化流动化学

Ashish Yewale¹, Yihui Yang², Neda Nazemifard²

¹Department of Chemical Engineering, Loughborough University, Loughborough, Leicestershire LE11 3TU, U.K.

ACS engineering Au

|June 25, 2025

概括

深度强化学习 (DRL) 优化了流动化学中的 imine 合成,大大减少了实验. 这种先进的机器学习方法提高了化学制造业的效率和可持续性.

科学领域:

化学工程是化学工程的重要组成部分.
机器学习机器学习
过程优化过程优化

背景情况:

流化学提供了成本效益和可持续的制造,但由于劳动密集型方法,在工艺开发方面面临挑战.
优化流体化学过程对于药品等关键化合物的高效合成至关重要.
机器学习集成可以减轻实验负担并提高过程效率.

研究的目的:

证明深度强化学习 (DRL) 是一种有效的自我优化策略,用于流动中的 imine 合成.
开发和评估一个深度决定性政策梯度 (DDPG) 代理,以优化流动反应堆条件.
为了比较DRL与传统优化方法的性能.

主要方法:

一种深度决定性政策梯度 (DDPG) 代理被设计用于通过与流动反应堆环境的相互作用来学习最佳操作条件.
使用实验数据开发了反应堆的数学模型,用于训练DDPG代理.
实现了新的自适应动态超参数调整,以提高DRL训练性能,并与贝叶斯优化和试错比较.
该DRL策略与无梯度方法 (SnobFit,Nelder-Mead) 相比进行了基准测试.

主要成果:

与Nelder-Mead和SnobFit.com相比,DDPG药物在胺基合成优化方面表现出优异的性能.
与Nelder-Mead相比,DRL方法将所需的实验数量减少了约50%,与SnobFit相比减少了75%.
DDPG代理显示了更好的全球解决方案跟踪,表明了增强的优化能力.

结论:

深度强化学习提供了一种强大,高效和可持续的方法来优化流体化学过程.
这种数据驱动的方法显著减少了实验工作量,提高了过程效率.
这些发现鼓励在化学过程设计和运行中更广泛地整合机器学习.

关键词:

贝叶斯优化是贝叶斯的优化.适应性的超参数调.深度决定性的政策梯度渐变.深度强化学习的学习.流动化学流动化学自我优化自我优化

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