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

Article


Valizadeh, A., Amirhosseini, M. H. and Ghorbani, Y. 2024. Predictive precision in battery recycling: unveiling lithium battery recycling potential through machine learning. Computers and Chemical Engineering. 183 (Art. 108623). https://doi.org/10.1016/j.compchemeng.2024.108623
AuthorsValizadeh, A., Amirhosseini, M. H. and Ghorbani, Y.
Abstract

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 pre-processing, feature engineering, and evaluation phases, catering to researchers with varying technical skills for effective model application in analysis and product development.

KeywordsLithium battery Recycling; Machine learning; Data-driven approach; Recycling potential prediction; Recycling LIB
JournalComputers and Chemical Engineering
Journal citation183 (Art. 108623)
ISSN0098-1354
Year2024
PublisherElsevier
Accepted author manuscript
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.1016/j.compchemeng.2024.108623
Publication dates
Online07 Feb 2024
PrintApr 2024
Publication process dates
Accepted06 Feb 2024
Deposited23 Feb 2024
Copyright holder© 2024, The Authors
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