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Machine learning to predict effective reaction rates in 3D porous media from pore structural features

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 direc...

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Detalles Bibliográficos
Autores principales: Liu, Min, Kwon, Beomjin, Kang, Peter K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8971379/
https://www.ncbi.nlm.nih.gov/pubmed/35361834
http://dx.doi.org/10.1038/s41598-022-09495-0
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author Liu, Min
Kwon, Beomjin
Kang, Peter K.
author_facet Liu, Min
Kwon, Beomjin
Kang, Peter K.
author_sort Liu, Min
collection PubMed
description 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 (ML). We first perform pore-scale reactive transport simulations with fluid–solid reactions in hundreds of porous media and calculate effective reaction rates from pore-scale concentration fields. We then train a Random Forests model with 11 pore structural features and effective reaction rates to quantify the importance of structural features in determining effective reaction rates. Based on the importance information, we train artificial neural networks with varying number of features and demonstrate that effective reaction rates can be accurately predicted with only three pore structural features, which are specific surface, pore sphericity, and coordination number. Finally, global sensitivity analyses using the ML model elucidates how the three structural features affect effective reaction rates. 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.
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spelling pubmed-89713792022-04-01 Machine learning to predict effective reaction rates in 3D porous media from pore structural features Liu, Min Kwon, Beomjin Kang, Peter K. Sci Rep Article 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 (ML). We first perform pore-scale reactive transport simulations with fluid–solid reactions in hundreds of porous media and calculate effective reaction rates from pore-scale concentration fields. We then train a Random Forests model with 11 pore structural features and effective reaction rates to quantify the importance of structural features in determining effective reaction rates. Based on the importance information, we train artificial neural networks with varying number of features and demonstrate that effective reaction rates can be accurately predicted with only three pore structural features, which are specific surface, pore sphericity, and coordination number. Finally, global sensitivity analyses using the ML model elucidates how the three structural features affect effective reaction rates. 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. Nature Publishing Group UK 2022-03-31 /pmc/articles/PMC8971379/ /pubmed/35361834 http://dx.doi.org/10.1038/s41598-022-09495-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Liu, Min
Kwon, Beomjin
Kang, Peter K.
Machine learning to predict effective reaction rates in 3D porous media from pore structural features
title Machine learning to predict effective reaction rates in 3D porous media from pore structural features
title_full Machine learning to predict effective reaction rates in 3D porous media from pore structural features
title_fullStr Machine learning to predict effective reaction rates in 3D porous media from pore structural features
title_full_unstemmed Machine learning to predict effective reaction rates in 3D porous media from pore structural features
title_short Machine learning to predict effective reaction rates in 3D porous media from pore structural features
title_sort machine learning to predict effective reaction rates in 3d porous media from pore structural features
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8971379/
https://www.ncbi.nlm.nih.gov/pubmed/35361834
http://dx.doi.org/10.1038/s41598-022-09495-0
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