Selective Alkaline Leaching of Mechanochemically Activated Spent LiCoO2 Cathodes: Taguchi Optimization and Machine-Learning-Assisted Interpretation
Lyazzat Mussapyrova, Rashid Nadirov, Matej Baláž, Kaster Kamunur, Aisulu Batkal, Yerzhan MukanovSelective alkaline leaching was evaluated as a pre-separation step for mechanochemically activated spent LiCoO2 cathodes. An L16(44) Taguchi design was used to assess the effects of the NaOH concentration, temperature, leaching time, and liquid-to-solid ratio on Li, Al, and Co extraction. The NaOH concentration was identified as the dominant factor controlling Li and Al extraction, whereas Co dissolution remained strongly suppressed under alkaline conditions. The best experimentally verified condition (6 mol L−1 NaOH, 80 °C, 90 min, L:S = 30 mL g−1) resulted in 82.96% Li extraction, 35.26% total Al extraction, and only 0.84% Co dissolution. Exploratory machine-learning analysis provided complementary support for the dominant influence of the NaOH concentration; however, because only 16 experiments were available, the models were not used as independent predictive optimization tools. The results demonstrate that mechanochemical activation followed by alkaline leaching can provide effective Li/Al–Co pre-separation by preferentially transferring Li and part of the Al inventory to the alkaline leachate while retaining most Co in the solid residue. Further downstream separation and purification of both streams are required before recoverable Li- and Co-containing products can be obtained.