On the Performance of Machine Learning Methods for Breakthrough Curve Prediction

TL;DR

This paper evaluates various machine learning techniques to predict breakthrough curves in reactive flows within porous media, focusing on the influence of Damköhler and Peclet numbers in both 1D and 3D scenarios.

Contribution

It provides a comprehensive analysis of machine learning methods for breakthrough curve prediction, including new insights into their performance in 1D and 3D reactive flow models.

Findings

ML methods can accurately predict breakthrough curves

Performance varies with flow parameters and dimensionality

The study highlights the most effective algorithms for this task

Abstract

Reactive flows are important part of numerous technical and environmental processes. Often monitoring the flow and species concentrations within the domain is not possible or is expensive, in contrast, outlet concentration is straightforward to measure. In connection with reactive flows in porous media, the term breakthrough curve is used to denote the time dependency of the outlet concentration with prescribed conditions at the inlet. In this work we apply several machine learning methods to predict breakthrough curves from the given set of parameters. In our case the parameters are the Damk\"ohler and Peclet numbers. We perform a thorough analysis for the one-dimensional case and also provide the results for the three-dimensional case.