Reactive Transport Modeling with Physics-Informed Machine Learning for Critical Minerals Applications
By: K. Adhikari , Md. Lal Mamud , M. K. Mudunuru and more
Potential Business Impact:
Simulates underground chemical reactions for mining.
This study presents a physics-informed neural network (PINN) framework for reactive transport modeling for simulating fast bimolecular reactions in porous media. Accurate characterization of chemical interactions and product formation in surface and subsurface environments is essential for advancing critical mineral extraction and related geoscience applications.
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