电池(电)
过程(计算)
机器学习
领域(数学分析)
计算机科学
人工智能
钥匙(锁)
资源(消歧)
持续性
系统工程
工程类
功率(物理)
物理
数学分析
计算机网络
生态学
数学
计算机安全
量子力学
生物
操作系统
作者
Alireza Valizadeh,Mohammad Hossein Amirhosseini,Yousef Ghorbani
标识
DOI:10.1016/j.compchemeng.2024.108623
摘要
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.
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