Lithium–Magnesium Separation in Salt Lake Brine Using Electrodialysis: A Multi‐Physical Modeling With Neural‐Network Prediction

ABSTRACT

Lithium resource serves as critical material for lithium‐ion battery industry, and it becomes indispensable for global technological progress and economic development. Facing the challenge of high magnesium‐to‐lithium ratio in salt lake brines of China, electrodialysis (ED) exhibits great potential as an extraction technology. However, lithium–magnesium separation using ED is a complicated multi‐physical process, and insufficient understanding of lithium extraction mechanism limits its efficiency improvement. Hence, the current work constructs a multi‐physical model for lithium extraction from high magnesium salt lake brine using ED to reveal ion migration behavior and separation mechanism under synergistic fields. On the basis of a parametric analysis with optimization, the magnesium‐to‐lithium ratio in brine is dramatically decreased from initial state at 105.05 to 1.87 at the outlet of ED device under the temperature of 323.15 K, potential difference of 0.5 V, and membrane thickness of 0.35 mm. Furthermore, five neural‐network models are developed as surrogates of the multi‐physical model using 2784 simulation‐generated samples without experimental data. Multidimensional dilated convolutional neural network (MDCNN) achieves the lowest in‐domain mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of 0.0293%, 0.0395%, and 0.7735%, respectively, whereas R ² (>0.998) provides limited discrimination. In 24 additional out‐of‐domain simulation cases, MDCNN achieves the lowest MAPE (3.8846%), whereas convolutional attention neural network (CNN‐Attention) achieves the lowest RMSE (0.2525) and the highest R ² (0.9494), demonstrating complementary generalization strengths. The current work offers a theoretical basis for optimizing lithium–magnesium separation performance using ED with neural‐network surrogate modeling, laying foundation for big data analysis and development of experimental correlations.