Simulation and Optimization of Bubble Dynamics for Energy-Saving Aluminum Electrolysis Based on PINN Method

Carbon dioxide produced in aluminum electrolysis greatly affects molten electrolyte flow, inter-electrode resistance and alumina dissolution, while the sealed high-temperature cell environment hinders real-time bubble monitoring. Traditional CFD simulation of this process requires high-precision meshes and therefore entails huge computational costs. This study adopts physics-informed neural networks (PINN) to combine physical governing equations with CFD data through loss functions, realizing mechanism-data fusion to explore gas-liquid flow characteristics in electrolyte melts. Driven by randomly sampled data, the model accurately predicts full-field internal flow fields using merely 2% of the original data, achieving relative RMSE and MAE both below 10%. Its prediction efficiency is about 3000 times higher than CFD. Simulation results indicate that bubbles drive electrolyte circulation. Rising bubble flow rate raises bubble volume fraction and layer thickness with a slowing growth trend. Bubble-induced extra resistance shows a nonlinear correlation with flow rate, with a critical value of 164.54 L/min; resistance surges rapidly once exceeding this point. Elevated current density also sharply increases extra resistance. Longitudinal-slot anodes can effectively reduce bubble resistance, reaching an 85.33% reduction at 180 L/min. Larger alumina particles present lower mass transfer efficiency and poorer dissolution performance. Rational regulation of bubble flow optimizes particle dissolution. The proposed bubble dynamic optimization method provides practical guidance for energy saving and output improvement in aluminum electrolysis industry.