Machine Learning to Predict Effective Reaction Rates in 3D Porous Media From Pore Structural Features

Machine Learning to Predict Effective Reaction Rates in 3D Porous Media From Pore Structural Features

Min Liu, Beomjin Kwon & Peter K. Kang

Large discrepancies between well-mixed reaction rates and effective reactions rates estimated under fluid flow conditions have been a major issue for predicting reactive transport in porous media systems. In this study, we introduce a framework that accurately predicts effective reaction rates directly from pore structural features by combining 3D pore-scale numerical simulations with machine learning. We show that effective reaction rates can be accurately predicted with only three pore structural features, which are specific surface, pore sphericity, and coordination number. The proposed framework enables accurate predictions of effective reaction rates directly from a few measurable pore structural features, and the framework is readily applicable to a wide range of applications involving porous media flows.

Scientific Reports volume 12, Article number: 5486 (2022)
Corresponding Author: Peter Kang

InterPore Members can promote their publications to the community via the InterPore In Journals Section of the Newsletter. If you wish to do so, please submit your publication highlight to newsletter@InterPore.org. Clearly indicate which of the authors is an InterPore member (or the institute with an Institutional Membership). Note that we will not review the entries nor does InterPore endorse the published work. Furthermore, we publish on a “submitted first, published first” basis. The highlighted publication should be no older than 6 months (available online).

The highlight should be short (max 100 words) and contain an illustration. Please note that we offer this opportunity exclusively to InterPore members. If you would like to become a member, please have a look here.