Developing physics-informed neural networks for model predictive control of periodic counter-current chromatography

Abstract

The applications of continuous manufacturing technology in biopharmaceuticals require advanced design, monitoring, and control due to its complexity. Traditional mechanistic models, which rely on numerical solutions, suffer from long computational times, making them unsuitable for the timely demands of continuous processes and digital twin applications in biomanufacturing. This issue significantly limits the capability for real-time optimization and control. To overcome this challenge, this study proposes a Physics-Informed Neural Network (PINN) based General Rate Model (GRM) approach that greatly reduces computation time while maintaining high accuracy and reliability in simulations. The developed PINN is applicable for different parameters across wide ranges and is capable of parameter estimation. It presents excellent performance in both offline simulation of single-column breakthrough curves and online optimization of load conditions for four-column periodic counter-current chromatography (4C-PCC), achieving significant reductions in fitting time from 2608.6 to 110.7 s for offline simulations, and completing online simulations within 12 to 14 s. The results demonstrate the potential of PINN for real-time model predictive control and digital twin applications, offering a promising solution to the limitations of traditional numerical methods.

Keywords: Continuous chromatography; Digital twin; General rate model; Model predictive control; Periodic counter-current chromatography; Physics-informed neural network.