[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119052-en":3,"doc-seo-119052-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119052,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Predictive precision in battery recycling - unveiling lithium battery recycling potential through machine learning","This paper explores machine learning for lithium battery recycling to strengthen sustainability and process efficiency. The research targets three areas: predicting recycling viability, optimizing recycling processes, and improving resource recovery; addressing engineering challenges through data-driven modeling; and proposing a streamlined application framework. Scientific principles, methodologies, and algorithms for battery recycling are analyzed, along with implementation implications and practical obstacles. Comparative results indicate the framework overcomes limitations of prior models by guiding preprocessing, feature engineering, and evaluation for varied technical expertise.","University of Dundee  \nPredictive precision in battery recycling  \nValizadeh, Alireza; Amirhosseini, Mohammad Hossein; Ghorbani, Yousef  \nDOI:  \n10.1016/j.compchemeng.2024.108623  \nPublication date:  \n2024  \nLicence:  \nCC BY-NC-ND  \nDocument Version  \nPeer reviewed version  \nLink to publication in Discovery Research Portal  \nCitation for published version (APA):  \nValizadeh, A. , Amirhosseini, M. H. , & Ghorbani, Y. (2024) . Predictive precision in battery recycling: unveiling lithium battery recycling potential through machine learning. Computers and Chemical Engineering, 183 , Article 108623. [https://doi.org/10.1016/j.compchemeng.2024.108623](https://doi.org/10.1016/j.compchemeng.2024.108623)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in Discovery Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 20. Apr. 2025  \nPredictive Precision in Battery Recycling: Unveiling Lithium Battery Recycling Potential through  \nMachine Learning  \nAlireza Valizadeh*a, Mohammad Hossein Amirhosseinib, Yousef Ghorbani c  \na Samad Power Ltd. 9 Centurion Ct, Brick Cl, Kiln Farm, Milton Keynes MK11 3JB, United Kingdom b Department of Computer Science and Digital Technologies, School of Architecture, Computing and Engineering, University of East London, London, E16 2RD, United Kingdom  \nc School of Chemistry, University of Lincoln, Joseph Banks Laboratories, Green Lane, Lincoln, Lincolnshire, LN6 7DL, United Kingdom  \nAbstract  \nThis 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.  \n(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.  \nKey words: Lithium battery; Recycling; Machine learning; Data-driven approach; Recycling potential prediction, Recycling LIB  \nThis is the accepted manuscript version of the following article: Valizadeh, A, Amirhosseini, MH & Ghorbani, Y 2024, 'Predictive precision in battery recycling: unveiling lithium battery recycling potential through machine learning', Computers and Chemical Engineering, vol. 183, 108623. [https://doi.org/10.1016/j.compchemeng.2024.108623](https://doi.org/10.1016/j.compchemeng.2024.108623)  \n1  \n© 2024 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0 license [https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)  \n1 Introduction  \nThe recycling of lithium batteries holds significant scientific importance and has a crucial background [1] . With the increasing adoption of lithium batteries in various applications, such as electric vehicles, the need for efficient and sc","cbCaiq66uLVfk4LP","https://ap.wps.com/l/cbCaiq66uLVfk4LP","pdf",649099,1,37,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What is the main goal of the paper on battery recycling?\",\"answer\":\"The paper applies machine learning to lithium battery recycling to improve sustainability and process efficiency, with a focus on predicting viability and enhancing resource recovery.\"},{\"question\":\"Which key areas does the study address?\",\"answer\":\"It covers predicting recycling potential, optimizing recycling processes, addressing engineering challenges, and introducing a streamlined framework for applying machine learning in this domain.\"},{\"question\":\"What advantages does the proposed framework provide?\",\"answer\":\"Comparative analysis shows it offers benefits over earlier models and specifically supports practical steps such as preprocessing, feature engineering, and evaluation for effective model use.\"}]","Predictive precision in battery recycling - 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