Reactive Transport Modeling with Physics-Informed Machine Learning for Critical Minerals Applications

TL;DR

This paper introduces a physics-informed neural network framework to simulate reactive transport processes, enabling more accurate modeling of chemical reactions in porous media crucial for critical mineral extraction.

Contribution

The study develops a novel PINN-based approach for reactive transport modeling, integrating physical laws directly into neural networks for improved accuracy.

Findings

Enhanced simulation accuracy for bimolecular reactions in porous media

Potential for improved mineral extraction process modeling

Framework adaptable to various geoscience applications

Abstract

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.