Can a Computer “Learn” Nonlinear Chromatography?: Experimental Validation of Physics-Based Deep Neural Networks for the Simulation of Chromatographic Processes

Abstract

This article presents the capabilities of machine learning in addressing the challenges related to the accurate description of adsorption equilibria in the design of chromatographic processes. Our previously developed physics-based artificial neural network framework for adsorption and chromatography emulation (PANACHE) approach is extended to simulate the dynamics of chromatographic columns without using adsorption isotherms. The incorporation of underlying conservation laws in the form of a physics-constrained loss function during training enables PANACHE models to infer the complex dynamics of chromatographic columns, even without the knowledge of adsorption isotherms. The isotherm-agnostic abilities of PANACHE models are tested by considering two binary systems with complex adsorption equilibria: 1) mixed Langmuir M1-type binary system and 2) Tröger’s base enantiomers on Chiralpak AD that show inflection in the isotherm. For each case study, unique feed-forward deep neural networks with an input layer, five hidden layers, and an output layer are trained for two solute components using the elution data either from simulations or experiments. The results show that PANACHE models are successful in predicting the dynamics of binary solute mixtures in chromatographic columns even in the absence of adsorption isotherms, with prediction errors in the order of 10–2 in the measure of relative L2-norm, for most of the cases. The ability of PANACHE models to infer the adsorption isotherms is also demonstrated.