Can a computer "learn" nonlinear chromatography?: Physics-based deep neural networks for simulation and optimization of chromatographic processes

The design and optimization of chromatographic processes is essential for enabling efficient separations. To this end, hyperbolic partial differential equations (PDEs) along with nonlinear adsorption isotherms must be solved using computationally expensive numerical solvers to understand, simulate, and design the complex behavior of solute movement in chromatographic columns. In this study, physics-based artificial neural network framework for adsorption and chromatography emulation (PANACHE) is used to simulate and optimize chromatographic processes in a computationally faster and reliable manner. The proposed approach relies on learning the underlying PDEs in the form of a physics-constrained loss function to improve the accuracy of process simulations. The effectiveness of this approach is demonstrated by considering the complex dynamics of binary solute mixtures for generic pulse injections subjected to different isotherm systems, namely, the four cases of the generalized Langmuir isotherms. Unique neural network models were developed for each isotherm and the models accurately predicted the spatiotemporal concentrations of solute mixture in chromatographic columns for an arbitrary feed concentrations and injection volumes by facilitating up to 250 times computational speed-ups. Moreover, the neural network models were incorporated with process optimization routines to precisely determine the optimal injection volumes to enable baseline separation of solute components of the feed mixture.