基于物理学的神经网络来解决染色学过程中的一次性运动模型
Si-Yuan Tang1, Yun-Hao Yuan2, Yu-Cheng Chen3
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
|September 16, 2023
概括
基于物理学的神经网络 (PINNs) 为模拟色谱过程提供了比传统的数值方法更快,更准确的替代方案. 这项研究展示了一种用于实时数字双胞胎应用的新型集体运动模型-PINN (LKM-PINN).
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算科学 计算科学
背景情况:
- 使用数值方法进行染色学过程模拟是计算密集的,缺乏实时响应.
- 基于物理学的神经网络 (PINNs) 将物理定律与神经网络结构相结合,为科学问题解决提供准确性和速度之间的平衡.
研究的目的:
- 研究PINNs用于模拟色谱过程的应用,特别关注一次性运动模型 (LKM).
- 开发和优化PINN模型 (LKM-PINN) 以准确和快速预测色谱突破曲线.
主要方法:
- 设计并优化了针对LKM量身定制的PINN结构,包括网络架构,训练数据分布 (强调突破过渡) 和模型复杂性.
- 开发了一个LKM-PINN模型,包括四个神经网络,12个层和606个神经元.
- 使用突破曲线和在不同条件下推断的性能 (停留时间,度,列大小) 估计的LKM参数.
主要成果:
- 该LKM-PINN模型实现了与数值方法相比较的准确性,平均适配误差较低 (0.075比0.081).
- 显著提高了装配速度:LKM-PINN的160秒,而数值方法的7-72分钟.
- 通过LKM-PINN.的随机初始猜测,进一步提高速度至30秒.
结论:
- 开发的LKM-PINN模型在染色学模拟方面表现出高精度和极高的速度.
- LKM-PINN适用于实时模拟,可以实现诸如染色学中的数字双胞胎等应用.
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