开发基于物理学的神经网络,用于定期逆流色谱的模型预测控制
Si-Yuan Tang1, Yun-Hao Yuan2, Yan-Na Sun2
1College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China; Manufacturing Science and Technology, Global Manufacturing, WuXi Biologics, Wuxi 214000, China.
Journal of chromatography. A
|November 20, 2024
概括
物理信息神经网络 (PINNs) 加快生物制造模拟. 这种新的方法大大减少了连续流程的计算时间,从而实现实时优化和数字双胞胎应用.
科学领域:
- 生物制药制造业 生物制药制造业
- 过程建模和模拟的过程.
- 化学工程中的人工智能.
背景情况:
- 生物制药的连续制造需要复杂的设计,监测和控制.
- 传统的机械模型是计算密集型的,阻碍了实时应用.
- 这种限制影响了生物制造中的优化和控制.
研究的目的:
- 为生物制药连续制造开发一个计算高效的建模方法.
- 为了解决实时过程控制中传统数值方法的局限性.
- 为了实现先进的应用程序,如生物制造中的数字双胞胎.
主要方法:
- 实现与一般速率模型 (GRM) 集成的物理信息神经网络 (PINN).
- 基于PINN的GRM用于准确和快速模拟色谱过程.
- 在线模拟突破曲线和在线优化四列周期反流色谱 (4C-PCC) 的应用.
主要成果:
- 显著减少模拟装配时间:离线模拟从2608.6s降至110.7s.
- 在12-14秒内成功在线模拟4C-PCC.
- PINN证明了在各种参数和有效参数估计中广泛的适用性.
结论:
- 基于PINN的GRM为传统模型提供了一个高度准确和可靠的替代方案.
- 这种方法显著降低了生物制造模拟的计算负担.
- PINN显示了实时模型预测控制和生物过程中数字双胞胎开发的巨大潜力.
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