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

20 March 2023, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

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.

Keywords

Preparative Chromatography
Machine Learning
Artificial Neural Networks
Modelling

Supplementary materials

Title
Description
Actions
Title
Supporting Information
Description
This file contains supporting information to the main article.
Actions

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.