深度强化学习用于液体色谱中的梯度分离的直接优化
Alexander Kensert1, Pieter Libin2, Gert Desmet3
1University of Leuven (KU Leuven), Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, Herestraat 49, 3000 Leuven, Belgium.
Journal of chromatography. A
|March 5, 2024
概括
强化学习 (RL) 被引入液体染色学以优化分离梯度. 靠近政策优化 (PPO) 代理商通过从侦察运行中学习最佳梯度程序来提高峰值分辨率.
科学领域:
- 分析化学 分析化学
- 分离科学 分离科学
- 染色体学 染色体学 是一种染色体学.
背景情况:
- 强化学习 (RL) 已经在复杂的任务中取得了成功,但在分离科学中基本上尚未被探索.
- 在液态染色学中优化分离梯度对于解决复杂混合物至关重要.
- 传统的梯度优化方法可能耗时,可能不会产生最佳结果.
研究的目的:
- 引入近距离政策优化 (PPO),一种RL,液态染色学.
- 为了评估PPO优化分离梯度的能力,基于单个侦察运行.
- 调查PPO是否可以通过学习定制的梯度程序来提高分离分辨率.
主要方法:
- 培训PPO代理人选择线性和多段 (2,3,16段) 的梯度程序.
- 优化基于初始通用线性梯度的结果.
- 代理人还使用连续实验 (2或3次运行) 进行训练,以进一步细化梯度.
主要成果:
- 通过选择优化梯度程序 (φ-程序),PPO代理成功地改善了分离.
- 多段梯度和连续实验导致奖励增加 (更好的分辨率),平均达到0.918.
- PPO显著优于随机和标准梯度优化方法,实现更高的平均回报.
结论:
- 邻近政策优化 (PPO) 显示了液体染色学中自动化梯度优化的巨大潜力.
- RL 剂可以学习为特定混合物量身定制分离条件,提高峰值分辨率.
- 这项研究为通过人工智能推进分离科学提供了一个有希望的新方向.
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