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相关概念视频

Feedback control systems01:26

Feedback control systems

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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
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Open and closed-loop control systems01:17

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Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
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Precipitate Formation and Particle Size Control01:16

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In precipitation gravimetry, the precipitating agent should react specifically or selectively with the analyte. While a specific reagent reacts with the analyte alone, a selective reagent can react with a limited number of chemical species.
The obtained precipitate should be either a pure substance of known composition or easily converted to one by a simple process, such as ignition or drying. In addition, the precipitate should be insoluble and easily filterable. In general, filterability...
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相关实验视频

Updated: Jan 15, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Published on: August 29, 2025

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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
PubMed
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).

关键词:
连续颗粒制造的连续颗粒制造.机器学习 机器学习制药制造业 制药制造业 制药制造业过程控制 过程控制过程开发 过程开发

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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.