Flotation separation of lithium–ion battery electrodes predicted by a long short-term memory network using data from physicochemical kinetic simulations and experiments

• Published in: Journal of industrial information integration Vol. 42; p. 100697
• Main Authors: Gomez-Flores, Allan, Park, Hyunsu, Hong, Gilsang, Nam, Hyojeong, Gomez-Flores, Juan, Kang, Seungmin, Heyes, Graeme W., Leal Filho, Laurindo de S., Kim, Hyunjung, Lee, Jung Mi, Lee, Junseop
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.11.2024
• Subjects: Deep learning; Direct recycling; Froth flotation; Lithium-ion battery; Deep learning; Lithium-ion battery; Direct recycling; Froth flotation
• ISSN: 2452-414X
• DOI: 10.1016/j.jii.2024.100697

•Entrainment recovery was empirically included in an advanced flotation model.•Experiment and simulation data were used to train a deep long short-term memory neural network.•Software package integrating the neural network was developed to predict flotation hydrodynamics and probabilities, as well as pulp and entrainment recoveries.•Recovered anode materials were used to assemble half-cell coin cells.•Electrochemical performance of the cells was conducted and simulated. Anode and cathode active materials from spent lithium–ion batteries may be recovered and potentially used in new batteries to promote recycling and resource circulation. Froth flotation was applied to pristine active materials and the black mass obtained from pretreated spent batteries. Flotation kinetics was simulated with the use of computational fluid dynamics and surface chemistry. Bubble surface coverage and entrainment in the flotation kinetics model were selected and optimized by systematic fitting to experimental data. Entrainment influences the recovery and grade of the active materials. The optimized flotation kinetics model was used for generating additional data that, along with the fitted data, were used to train a deep learning neural network. The trained network was validated using anode–cathode and black mass flotation experiments, and its predictions showed a maximum residual error of 0.18 ± 0.11 recovery. The simulation framework was developed into a desktop application that predicts the flotation behavior of active materials. It provides information for estimating results following operational and physicochemical changes and for optimizing flotation processes. Finally, recovered anode active materials from black mass were selected for coin cell tests. The coulombic efficiency of these coin cells was initially lower (86.8 %) than that of cells made with pristine graphite particles (98.4 %). [Display omitted]