Bulk micro-nano-bubbles (BMNBs) have been proven to be effective at improving the flotation recovery and kinetics of fine-grained minerals. However, there is currently no research reported on the correlation between the properties of BMNBs and flotation performance. For this purpose, aqueous dispersions with diverse properties were created by altering preparation time (0, 1, 2, 3, 5, and 7 min), aeration rate (0, 0.5, 1, 1.5, and 2 L/min) and aging time (0, 0.5, 1, and >3 min). Micro- and nano-bubbles were characterized using focused beam reflection measurements (FBRM) and nanoparticle tracking analysis (NTA), respectively. The micro-flotation of quartz particles was performed using an XFG-cell in the presence and absence of BMNBs with Cetyltrimethylammonium bromide (CTAB) as a collector. The characterization of bubble sizes showed that the bulk micro-bubble (BMB) and bulk nanobubble (BNB) diameters ranged from 1–10 μm and 50–400 nm, respectively. It was found that the preparation parameters and aging time considerably affected the number of generated bubbles. When BNBs and BMBs coexisted, the recovery of fine quartz particles significantly improved (about 7%), while in the presence of only BNBs the promotion of flotation recovery was not significant (2%). This was mainly related to the aggregate via bridging, which was an advantage for quartz flotation. In comparison, no aggregates were detected when only nano-bubbles were present in the bulk solution.

The present study investigates the optimization and advanced simulation of the flotation process of coarse particles (–425 + 106) using micro-nanobubbles (MNBs). For this purpose, flotation experiments in the presence and absence of MNBs were performed on coarse quartz particles, and the results were statistically analyzed. Methyl isobutyl carbinol (MIBC) was employed as a frother for generating MNBs through hydrodynamic cavitation. The significance of the operating variables, including impeller speed, air flow rate, together with the bubble size, and particle size on the flotation recovery was assessed using historical data (HD) design and analysis of variance (ANOVA). The correlation between the flotation parameters and process response in the presence and absence of MNBs was modeled using hybrid convolutional neural networks (CNNs) and recurrent neural networks (RNNs) as the deep learning (DL) frameworks to automatically extract features from input data using a CNN as the base layer. The ANOVA results indicated that all variables affect process responses statistically and meaningfully. Significant interactions were found between air flow rate and particle size as well as impeller speed and MNB size. It was found that a CNN-RNN model could finally be used to model the process based on the intelligent simulation results. Based on Pearson correlation coefficients (PCCs), it was evident that particle size had a strong linear relationship with recovery. However, Shapley additive explanations (SHAP) was considerably more accurate in predicting relationships than Pearson correlations, even though the model outputs agreed well.

The rapid growth of tennis participation and the widespread use of lithium-ion batteries have led to increasing volumes of rubber waste and spent battery materials, underscoring the need for integrated recycling strategies. In this work, a thermochemical co-pyrolysis process is developed to convert waste tennis ball rubber particles (TBRPs) and spent lithium-ion battery (LIB) cathodes into valuable products. The decomposition of TBRPs generates reactive gaseous and liquid hydrocarbons that function as in situ reductants, enabling the breakdown of high-valence transition metal oxides in the cathode material. Subsequent magnetic separation and mild acid-washing yield nonmagnetic solids enriched in lithium compounds and carbonaceous residues. Structural and chemical analyses (SEM, XRD, TEM, EDS, and XPS) confirm extensive cathode reduction and the formation of Li₂CO₃ at optimized conditions (650 °C, 1 h, cathode-to-TBRPs mass ratio 1:0.65). The carbonized rubber evolves into a highly porous carbon material with a carbon purity of approximately 95.37 At%. This study demonstrates a low-energy, environmentally friendly pathway for the co-valorization of two challenging waste streams while simultaneously recovering lithium salts, reduced metal oxides, and functional porous carbon.