Synergistic effect of grinding time and submicron (nano) bubbles on the zeta potential state of spent lithium-ion batteries: A gene expression programming approach

The present work aims at the investigation and advanced simulation of the synergistic effect of grinding conditions and sub-micron (nano) bubbles (NBs) on the zeta potential mechanism of spent lithium-ion batteries (LIBs). For this purpose, the variation of the zeta potential of electrode active materials under different conditions was measured based on an effective Historical Data (HD) experimental design . The impact of operating variables including grinding time (0–25 min), pH (4.5–11.5), collector type (collectorless, n-dodecane, kerosene, and diesel oil), and NBs (absence and presence) were assessed through one-way analysis of variance (ANOVA). The process was then simulated using a genetic algorithm (GA) as an optimization algorithm of the artificial neural network (ANN). The statistical results indicated that the process was significantly influenced by pH, collector, and NBs (p value  < 0.05) through a nonlinear trend. Although the individual effect of grinding time was not significant, a complex nonlinear interaction between grinding time and pH was observed. The effect of collectors followed the order of diesel oil > n-dodecane > kerosene; however, the effect of collectors was inverted at a pH of 10. Finally, the intelligent simulation results revealed that the process could be modeled using a genetic algorithm with a determination coefficient and error of 94.44 % and 3.28 %, respectively.