RESUMEN
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.
RESUMEN
Model-based design and optimization methods facilitate industrial applications of chromatographic separations. The uncertainty of the model parameters must be quantified to ensure robust design and control. In this study, we propose an approach using the sequential Monte Carlo (SMC) method based on the Bayesian principle to estimate the uncertainty of the parameters. The linear driving force model for separation of phenol and p-cresol was considered as an example. By comparing different injection tests, we confirmed the necessity of pulse injection and breakthrough experiments to estimate parameters with sufficient accuracy and precision. We also found that modeling observation errors carefully is critical to obtain reasonable estimation.