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RESEARCH PAPER · Journal of Cleaner Production · 2023

Application of machine learning to guide efficient metal leaching from spent lithium-ion batteries and comprehensively reveal the process parameter influences

Bo Niu, Xiaomin Wang, Zhenming Xu

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Application of machine learning to guide efficient metal leaching from spent lithium-ion batteries and comprehensively reveal the process parameter influencesOriginalReview of Ultrasound-Assisted Recycling and Utilization of Cathode Materials from Spent Lithium-Ion Batteries: State-of-the-Art and Outlook54Intelligent leaching rare earth elements from waste fluorescent lamps22Fundamentals of the recycling of spent lithium-ion batteries198Predictive precision in battery recycling: unveiling lithium battery recycling potential through machine learning22Towards a Taxonomy of E-Waste Urban Mining Technology Design and Adoption: A Systematic Literature Review14Perspective on recycling technologies for critical metals from spent lithium-ion batteries24Recent and Novel Leaching Processes for Recovery of Metals from Spent Lithium-ion Batteries: A Review7Assessing and explaining rising global carbon sink capacity in karst ecosystems27Cutting-edge technologies for the recovery of cobalt from different waste discards0Application of machine learning for environmentally friendly advancement: exploring biomass-derived materials in wastewater treatment and agricultural sector − a review9Recent Advancements in Artificial Intelligence in Battery Recycling53Machine learning approach for revealing the nickel grade and recovery optimization in reduction process of laterite ores3Selective Recovery of Zinc from Alkaline Batteries via a Basic Leaching Process and the Use of a Machine Learning-Based Digital Twin for Predictive Purposes8From lithium-last technology to lithium-first technology: technical mapping and collaborative strategies for sustainable lithium-ion battery recycling13Intelligent metal recovery from spent Li-ion batteries: machine learning breaks the barriers of traditional optimizations17Mechanism-infused machine learning for scattered metal recovery: SHAP decodes multiscale physicochemical drivers in refractory matrices1Optimizing Cobalt Recovery from Spent Lithium-Ion Batteries Using Machine Learning and Multi-Objective Optimization0Interpretable Machine Learning Models Delivering a New Perspective for the Reaction Mechanism between Organic Pollutants and Oxidative Radicals39Integration of lithium-ion battery recycling into manufacturing through digitalization: A perspective22Critical metal extraction from spent battery cathodes and anticipated developments using next generation green solvents for achieving a net-zero future21Accelerated Screening of Deep Eutectic Solvents for Nickel/Cobalt Extraction: Integrating Quantum Chemistry, Machine Learning, and Experimental Validation4Upcycling and recycling of spent battery waste for a sustainable future: Progress and perspectives51Machine learning for efficient metal leaching from spent LiFePO4: Predictive modeling and sustainability assessment20Machine learning-guided rare earth recovery from NdFeB magnet waste: Model development, parameter influence analysis and experimental validation5Sustainable recovery of Cu, Ag and Au from the waste printed circuit boards and process optimisation by machine learning11Sustainable innovation in the lithium industry through data science: perspectives and challenges1Sustainable nickel recovery: Advancing bioleaching techniques for secondary sources in renewable energy and circular economy applications7Constructing advanced oxidation field by ultrasound towards separation of lithium from spent lithium iron phosphate30Research on the evaluation method system for recycling and utilization of spent lithium battery: Multi method collaboration and data-driven innovation path5AI‐Driven Advances in Sustainable Materials for Green Energy: From Innovation to Lifecycle Management31Machine learning for efficient prediction and optimization of platinum group metal recovery from spent automotive catalysts4Application of shrinking core and soft computing models (ANFIS and ANN) for the leaching of copper (II) from artificially contaminated soil2Machine Learning-Driven Inverse Design for Low-Carbon and Cost-Effective Organic Acid Leaching of Spent Ternary Lithium Batteries8Predictive modeling and graphical user interface-based interpretation of valuable metal leaching efficiency from coal gangue using machine learning5An applied machine learning framework for waste lithium-ion battery leaching with integrated preliminary environmental and economic assessment10Machine-learning-assisted optimization of glutamate leaching followed by sulfide precipitation for base metal recovery from waste printed circuit boards0Chelating extraction of critical metals from cathode of end-of-life lithium titanium oxide batteries: Experiments, machine learning and validation5Accurate prediction of NCM batteries recovery process under machine learning: Mechanism analysis and industrial application3Enhancing the circular economy of lithium-ion batteries: a data-driven optimisation of leaching efficiency using kernel ridge regression5Machine Learning-Accelerated Development of Li-Rich Layered Oxides with High Reversible Capacity and Coulombic Efficiency3Predicting copper leaching from slag: an interpretable machine learning approach under oxidative sulfuric acid conditions1Two-stage interpretable machine learning for hydrometallurgical recycling of spent LiFePO 4 batteries: integrating pretreatment and leaching for process optimization0Interpretable machine learning enables mechanistic insights and predictive control for clean and sustainable copper electrorefining5Closing the loop: A systematic review of artificial intelligence in circular e-waste management3Comparison of Zn recovery prediction from carbonate ores with machine-learning methods1Machine learning-assisted design of ozone catalyst: TiO₂/AC -based nanocomposites for BAA degradation as an example2Cavitation-induced reactive microenvironments in metal leaching: from bubble dynamics to multiscale mechanistic modeling1Predictive Modeling and SHAP-Based Interpretability of Manganese and Iron Dissolution in Multi-Acid Leaching Systems Using Hybrid Machine Learning1Decoding hydrometallurgical kinetics of spent lithium-ion batteries via time-series generative adversarial network-driven data augmentation and machine learning to address data scarcity0Toward Next Generation Lithium‐Ion Battery Recycling: From Metal Extraction to In Situ Cell Repair4Multi-objective environmental-economic optimization of rare metal recovery from phosphogypsum: Techno-economic and life cycle assessment driven by machine learning0From single physical-assisted technology to multivariate collaboration: mechanism, progress, and intelligent path of recycling technology for cathode materials of spent lithium-ion batteries0Interpretable Machine-Learning-Assisted Analysis of Caustic Leaching for Purification of Spent Cathode Carbon0Optimization and modeling of the leaching process for South African iron–manganese ore using machine learning–assisted reduced iron powder0Sustainable Regeneration of Graphite from Spent Lithium-Ion Batteries through Machine Learning and Life Cycle Assessment0Data driven low carbon design of FeCo catalysts for ciprofloxacin degradation: Bridging causal machine learning and atomistic insights0From recovery to qualification: Feedstock disorder and the battery-grade gap in lithium-ion battery recycling0Advances in green electrochemical pathways for sustainable mineral and metal processing0

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  2. Intelligent leaching rare earth elements from waste fluorescent lamps

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  3. Fundamentals of the recycling of spent lithium-ion batteries

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  4. Predictive precision in battery recycling: unveiling lithium battery recycling potential through machine learning

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  5. Towards a Taxonomy of E-Waste Urban Mining Technology Design and Adoption: A Systematic Literature Review

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  7. Recent and Novel Leaching Processes for Recovery of Metals from Spent Lithium-ion Batteries: A Review

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  8. Assessing and explaining rising global carbon sink capacity in karst ecosystems

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  9. Cutting-edge technologies for the recovery of cobalt from different waste discards

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  10. Application of machine learning for environmentally friendly advancement: exploring biomass-derived materials in wastewater treatment and agricultural sector − a review

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  11. Recent Advancements in Artificial Intelligence in Battery Recycling

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  12. Machine learning approach for revealing the nickel grade and recovery optimization in reduction process of laterite ores

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  13. Selective Recovery of Zinc from Alkaline Batteries via a Basic Leaching Process and the Use of a Machine Learning-Based Digital Twin for Predictive Purposes

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  14. From lithium-last technology to lithium-first technology: technical mapping and collaborative strategies for sustainable lithium-ion battery recycling

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  15. Intelligent metal recovery from spent Li-ion batteries: machine learning breaks the barriers of traditional optimizations

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  16. Mechanism-infused machine learning for scattered metal recovery: SHAP decodes multiscale physicochemical drivers in refractory matrices

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  17. Optimizing Cobalt Recovery from Spent Lithium-Ion Batteries Using Machine Learning and Multi-Objective Optimization

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  18. Interpretable Machine Learning Models Delivering a New Perspective for the Reaction Mechanism between Organic Pollutants and Oxidative Radicals

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  19. Integration of lithium-ion battery recycling into manufacturing through digitalization: A perspective

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  20. Critical metal extraction from spent battery cathodes and anticipated developments using next generation green solvents for achieving a net-zero future

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  21. Accelerated Screening of Deep Eutectic Solvents for Nickel/Cobalt Extraction: Integrating Quantum Chemistry, Machine Learning, and Experimental Validation

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  23. Machine learning for efficient metal leaching from spent LiFePO4: Predictive modeling and sustainability assessment

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  24. Machine learning-guided rare earth recovery from NdFeB magnet waste: Model development, parameter influence analysis and experimental validation

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  25. Sustainable recovery of Cu, Ag and Au from the waste printed circuit boards and process optimisation by machine learning

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  31. Machine learning for efficient prediction and optimization of platinum group metal recovery from spent automotive catalysts

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  36. Machine-learning-assisted optimization of glutamate leaching followed by sulfide precipitation for base metal recovery from waste printed circuit boards

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  37. Chelating extraction of critical metals from cathode of end-of-life lithium titanium oxide batteries: Experiments, machine learning and validation

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  42. Two-stage interpretable machine learning for hydrometallurgical recycling of spent LiFePO 4 batteries: integrating pretreatment and leaching for process optimization

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  48. Predictive Modeling and SHAP-Based Interpretability of Manganese and Iron Dissolution in Multi-Acid Leaching Systems Using Hybrid Machine Learning

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  49. Decoding hydrometallurgical kinetics of spent lithium-ion batteries via time-series generative adversarial network-driven data augmentation and machine learning to address data scarcity

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  50. Toward Next Generation Lithium‐Ion Battery Recycling: From Metal Extraction to In Situ Cell Repair

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