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

This paper explores the application of machine learning in battery recycling, aiming to enhance sustainability and process efficiency. The research focuses on three key areas: (i) Investigating machine learning's potential in predicting battery recycling viability, optimizing processes, and improving resource recovery. (ii) Assessing machine learning's impact on addressing engineering challenges within recycling. (iii) Introducing a streamlined framework for the application of machine learning in this domain. The study comprehensively analyzes scientific principles, methodologies, and algorithms relevant to battery recycling. Furthermore, it examines practical implications and challenges associated with implementing machine learning techniques in real-world scenarios. Our comparative analysis reveals that the proposed framework offers numerous advantages and effectively addresses common limitations seen in previous models. Notably, this framework provides detailed insights into preprocessing, feature engineering, and evaluation phases, catering to researchers with varying technical skills for effective model application in analysis and product development.