11th World Congress on Industrial Process Tomography
Learning to reconstruct the bubble distribution with conductivity maps using Invertible Neural Networks and Error Diffusion
N. Kumar1*, L. Krause2,3, T. Wondrak3, S. Eckert3, K. Eckert2,3, S. Gumhold1
1Chair of Computer Graphics and Visualization, TU Dresden, Germany
2Institute of Process Engineering and Environmental Technology, TU Dresden, Germany
3Institute of Fluid Dynamics, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
*Email: nishant.kumar@tu-dresden.de
ABSTRACT
Electrolysis is crucial for eco-friendly hydrogen production, but gas bubbles generated during the process hinder reactions, reduce cell efficiency, and increase energy consumption. Additionally, these gas bubbles cause changes in the conductivity inside the cell, resulting in corresponding variations in the induced magnetic field around the cell. Therefore, measuring these gas bubble-induced magnetic field fluctuations using external magnetic sensors and solving the inverse problem of Biot-Savart’s Law allows for estimating the conductivity in the cell and, thus, bubble size and location. However, determining high-resolution conductivity maps from only a few induced magnetic field measurements is an ill-posed inverse problem. To overcome this, we exploit Invertible Neural Networks (INNs) to reconstruct the conductivity field. Our qualitative results and quantitative evaluation using random error diffusion show that INN achieves far superior performance compared to Tikhonov regularization.
Keywords: Machine Learning, Invertible Neural Networks, Water Electrolysis, Biot-Savart Law
Industrial Application: Clean Energy
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