机器学习实时控制连续颗粒化工艺的过程
Maksym Dosta1, Moritz Schneider2, Christopher W Geis2
1Pharmaceutical Development CMC NCE, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397 Biberach an der Riss, Germany.
International journal of pharmaceutics
|October 6, 2025
概括
本研究介绍了一种机器学习 (ML) 模型,用于连续制药制造中的实时控制. 开发的系统有效地调整关键过程参数 (CPPs),以在湿颗粒中实现所需的关键材料属性 (CMAs).
科学领域:
- 制药制造业 制药制造业 制药制造业
- 过程控制 过程控制
- 机器学习应用 机器学习应用
背景情况:
- 连续制造需要高效的工艺开发和操作,这在理解关键工艺参数 (CPP) 和关键材料属性 (CMA) 方面带来了挑战.
- 实施主动过程控制对于在复杂的制药工厂中保持稳定的控制状态至关重要.
研究的目的:
- 使用机器学习 (ML) 开发数据驱动的过程模型,以实时控制连续湿颗粒线.
- 将机械模型集成为软传感器,以增强ML模型培训,并创建混合架构.
主要方法:
- 利用历史过程数据和有针对性的新数据收集来构建一个ML内核.
- 实施了基于开发的ML模型的颗粒加工厂的控制系统.
- 用机械模型 (软传感器) 扩展过程数据,用于混合模型方法.
主要成果:
- 成功构建了一个ML内核,并实施了连续颗粒工厂的控制系统.
- 混合模型架构有效地支持ML培训.
- 证明了连续工厂的高效实时控制.
结论:
- 拟议的基于机器学习的策略能够有效地实时控制连续的制药制造流程.
- 开发的系统可以通过调整关键过程参数 (CPP) 来实现所需的关键材料属性 (CMA),例如颗粒大小和干燥损失.
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