Learning to reconstruct the bubble distribution with conductivity maps using Invertible Neural Networks and Error Diffusion
This addresses efficiency and energy consumption issues in eco-friendly hydrogen production by improving bubble monitoring, though it is an incremental application of existing methods to a new domain.
The paper tackled the ill-posed inverse problem of reconstructing high-resolution conductivity maps from few magnetic field measurements to estimate bubble distribution in electrolysis, achieving far superior performance compared to Tikhonov regularization.
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 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.