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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Bibliographic Details
Published in:Scientific reports 2022-03, Vol.12 (1), p.5486-5486, Article 5486
Main Authors: Liu, Min, Kwon, Beomjin, Kang, Peter K.
Format: Article
Language:English
Subjects:
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704/172
704/242
704/47
Chemical reactions
Culture Media
Fluid flow
Humanities and Social Sciences
Learning algorithms
Machine Learning
Models, Theoretical
multidisciplinary
Neural networks
Permeability
Porosity
Porous media
Science
Science (multidisciplinary)
Sensitivity analysis
Simulation
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-09495-0