Department of Materials Process Engineering, Nagoya University, Furo-cho 1, Chikusa, Nagoya, Aichi, 464-8603 Japan.
Department of Materials Process Engineering, Nagoya University, Furo-cho 1, Chikusa, Nagoya, Aichi, 464-8603 Japan; School of Engineering Science, LUT University, Mukkulankatu 19, 15210 Lahti, Finland.
J Chromatogr A. 2024 Aug 16;1730:465077. doi: 10.1016/j.chroma.2024.465077. Epub 2024 Jun 12.
Chromatographic separation processes are most often modeled in the form of partial differential equations (PDEs) to describe the complex adsorption equilibria and kinetics. However, identifying parameters in such a model requires substantial computational effort. In this work, a novel parameter estimation approach using a Physics-informed Neural Network (PINN) model is developed and tested for a binary component system. Numerical accuracy of our PINN model is confirmed by validating its simulations against those of the finite element method (FEM). Furthermore, model parameters in the kinetic model are estimated by the PINN model with sufficient accuracy from the observed data at the column outlet, where parameter fitting error can be reduced by up to 35.0 % from the conventional method. In a comparison with the conventional numerical method, our approach can reduce the computational time by up to 95 %. The robustness of the PINN model has also been demonstrated by estimating model parameters from noisy artificial experimental data.
色谱分离过程通常采用偏微分方程（PDE）的形式进行建模，以描述复杂的吸附平衡和动力学。然而，确定此类模型中的参数需要大量的计算工作。在这项工作中，针对二元组分系统，开发并测试了一种使用物理信息神经网络（PINN）模型的新参数估计方法。通过将我们的 PINN 模型的模拟与有限元方法（FEM）的模拟进行验证，证实了其数值准确性。此外，通过从柱出口处的观测数据，使用 PINN 模型足够准确地估计了动力学模型中的模型参数，从而可以将参数拟合误差从传统方法降低多达 35.0%。与传统数值方法相比，我们的方法可以将计算时间减少多达 95%。通过从噪声人工实验数据中估计模型参数，还证明了 PINN 模型的稳健性。
