使用深度强化学习的可变液体色谱列的可适应的控制策略
David Andersson1, Christoffer Edlund2,3,4, Brandon Corbett5
1Sartorius Corporate Research, Umeå, Sweden. david.andersson@sartorius.com.
Scientific reports
|July 12, 2023
概括
本研究引入了使用深度强化学习的可适应色谱控制政策. 数据驱动的方法优化了可变列的流量,提高了生物治疗加工的生产率.
科学领域:
- 生物技术是生物技术.
- 化学工程是化学工程的重要组成部分.
- 过程控制 过程控制
背景情况:
- 用于生物治疗的染色学系统面临由于非线性行为和可变列属性的控制挑战.
- 没有内部列数据,实时控制是很困难的,这导致静态策略的性能不足.
- 适应每个列的静态策略需要昂贵的实验.
研究的目的:
- 制定可适应的,数据驱动的染色体系统控制政策.
- 为了克服生物处理中非线性动态和列变性所带来的挑战.
- 提高液体色谱的效率和生产率.
主要方法:
- 利用基于模拟的数据生成和深度强化学习 (DRL).
- 开发了一种控制器,可以操纵输入和输出流量,以优化奖励功能.
- 训练了DRL控制器在多种多样的染色体列上,具有很高的可变性.
主要成果:
- 在多个可变列中实现了有效的单一可适应的控制政策.
- 与人类设计的基准政策相比,表现出更高的生产率.
- 观察到纯度略有下降,同时生产率提高.
结论:
- 深度强化学习为在染色学中创建可适应的控制策略提供了一个有希望的方法.
- 数据驱动的方法为生物治疗下游加工提供了更有效的解决方案.
- 该战略解决了在动态生物处理环境中传统控制方法的局限性.
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