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 (pvalue < 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.