Optimization and digital twin of chromatography purification process using physics-informed neural networks

Abstract

The present disclosure relates to systems, methods, and products for optimization of a chromatography purification process using a physics-informed neural network. The method includes inputting a plurality of process parameters into the physics-informed neural network to obtain a predicted output; calculating a loss function based on a set of governing equations, as set of constraints, and the predicted output; determining whether the physics-informed neural network is convergent based on the calculated loss function; in response to the physics-informed neural network being convergent, exporting the physics-informed neural network; and in response to the physics-informed neural network not being convergent: updating a plurality of weights in the physics-informed neural network, and inputting the plurality of process parameters to the physics-informed neural network for a next convergence iteration to calculate the loss function and determine whether the physics-informed neural network is convergent.