Machine Learning Methods for Prediction of Breakthrough Curves in Reactive Porous Media

Abstract

Reactive flows in porous media play an important role in our life and are crucial for many industrial, environmental, and biomedical applications. Very often the concentration of the species at the inlet is known, and the so-called breakthrough curves, measured at the outlet, are the quantities which could be measured or computed numerically. The measurements and the simulations could be time-consuming and expensive, and machine learning approaches can help to predict breakthrough curves at lower costs. Methods like Gaussian processes and fully connected neural networks, as well as the cross-approximation tensor method, are well suited for predicting breakthrough curves (Fokina et al., On the performance of machine learning methods for breakthrough curve prediction (2022)). In this paper, we demonstrate their performance in the case of pore-scale reactive flow in catalytic filters.