Can a computer “learn” non-linear chromatography?: Experimental validation of physics-based deep neural networks for the simulation of chromatographic processes

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. 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. The ability of PANACHE models to infer the adsorption isotherms is also demonstrated.