Machine learning methods for prediction of breakthrough curves in reactive porous media
This work addresses the need for cost-efficient prediction of breakthrough curves in industrial, environmental, and biomedical applications, but it appears incremental as it applies existing methods to a specific domain.
The paper tackled the problem of predicting breakthrough curves in reactive porous media, which are time-consuming and expensive to measure or simulate, by applying machine learning methods like Gaussian processes and neural networks, achieving effective prediction for pore scale reactive flow in catalytic filters.
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 and Big Data approaches can help to predict breakthrough curves at lower costs. Machine learning (ML) methods, such as Gaussian processes and fully-connected neural networks, and a tensor method, cross approximation, are well suited for predicting breakthrough curves. In this paper, we demonstrate their performance in the case of pore scale reactive flow in catalytic filters.