相关实验视频
使用时间序列预测和机器学习技术预测青素度
Michail Rekkas-Ventiris1, Panorios Benardos1
1National Technical University of Athens, School of Mechanical Engineering, Manufacturing Technology Laboratory, Heroon Polytechniou 9, GR15772 Athens, Greece.
Drug development and industrial pharmacy
|March 6, 2026
概括
这项研究开发了一个Gated Recurrent Unit (GRU) 模型,用于准确预测药品发酵中的青素度. 该模型支持Pharma 4.0通过启用早期偏差检测和主动过程控制.
科学领域:
- 制药制造业 制药制造业 制药制造业
- 生物工艺工程 生物工艺工程
- 机器学习 机器学习
背景情况:
- 时间序列预测对于制药发酵质量控制至关重要.
- 行业向制药4.0的转变需要先进的监测和控制.
- 对于复杂的制药发酵,可靠的机器学习工具存在差距.
研究的目的:
- 开发一个基于Gated Recurrent Unit (GRU) 的机器学习模型.
- 在发酵过程中预测青素度.
- 支持制药制造业的优化和控制.
主要方法:
- 在模拟的青素发酵数据 (IndPenSim数据集) 上训练了一个GRU神经网络.
- 使用随机森林和XGBoost进行输入参数选择.
- 进行了系统的超参数调整,以优化模型.
主要成果:
- 通过优化GRU模型实现了7.20 × 10-4的平均平方误差 (MSE).
- 数据驱动的特征选择增强了预测准确性.
- 该模型在预测青素度方面表现出可靠性.
结论:
- GRU网络可用于发酵中的青素度预测.
- 格鲁模型作为实用的实时过程控制和偏差管理工具.
- 这种方法通过提高透明度和在受监管环境中实现数字双胞胎来支持Pharma 4.0.
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