Research on Magnetic Rollers for Recovering Non-Ferrous Metals from End-of-Life Vehicles Employing Machine Learning
Recovering copper foil and crushed aluminum from end-of-life vehicles (ELVs) is a significant issue in the recycling industry. As a key technology for sorting aluminum, copper, and other non-ferrous metals, eddy current separation (ECS) is efficient in isolating the non-ferrous metals according to t...
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| Veröffentlicht in: | Sustainability 2023-09, Vol.15 (18), p.13451 |
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| creator | Jia, Youdong Liu, Jianxiong Li, Zhengfang |
| description | Recovering copper foil and crushed aluminum from end-of-life vehicles (ELVs) is a significant issue in the recycling industry. As a key technology for sorting aluminum, copper, and other non-ferrous metals, eddy current separation (ECS) is efficient in isolating the non-ferrous metals according to their different electrical conductivity and density. However, further research is still needed in the separation of large-size copper foil and crushed aluminum from scrapped vehicles. In this study, support vector regression (SVR) and the sparrow search algorithm (SSA) are exploited for the first time to be used in optimizing the Halbach magnetic roller. Firstly, the numerical simulation results are based on the response surface methodology (RSM). Then, the accuracy of four kernel functions employing SVR is compared to select a kernel function. The sparrow search algorithm (SSA) is proposed to optimize the structural parameters of the Halbach magnetic roller, concentrating on the above-selected kernel function. Meanwhile, the parameters are confirmed. Numerical simulation results indicate that machine learning for magnetic roller optimization is feasible. |
| doi_str_mv | 10.3390/su151813451 |
| format | Article |
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Numerical simulation results indicate that machine learning for magnetic roller optimization is feasible.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su151813451</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Aluminum ; Artificial intelligence ; Efficiency ; Electrical conductivity ; Iron compounds ; Machine learning ; Magnetic fields ; Metals ; Nonferrous metal industry ; Numerical analysis ; Optimization ; Recycling ; Scrap ; Scrap metals ; Simulation ; Simulation methods ; Sustainability</subject><ispartof>Sustainability, 2023-09, Vol.15 (18), p.13451</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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Numerical simulation results indicate that machine learning for magnetic roller optimization is feasible.</description><subject>Algorithms</subject><subject>Aluminum</subject><subject>Artificial intelligence</subject><subject>Efficiency</subject><subject>Electrical conductivity</subject><subject>Iron compounds</subject><subject>Machine learning</subject><subject>Magnetic fields</subject><subject>Metals</subject><subject>Nonferrous metal industry</subject><subject>Numerical analysis</subject><subject>Optimization</subject><subject>Recycling</subject><subject>Scrap</subject><subject>Scrap metals</subject><subject>Simulation</subject><subject>Simulation methods</subject><subject>Sustainability</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpVkd9rAjEMx4-xwWTzaf9AYU9jnGus7V0fRXQTdAP34_Xo1ZxWzta1d2P-96u4B00eEpLPN4EkSe6A9hiT9Cm0wCEHNuBwkXT6NIMUKKeXJ_l10g1hQ6MxBhJEJwkLDKi8XhNnyVytLDZGk4Wra_SBVM6TBWr3g97YFXl1Np2g964NZI6NqiPh3ZaM7TJ1VTozFZIvXBtdYyDj7a52-4NsrvTaWCSzuMjGwm1yVUUtdv_jTfI5GX-MXtLZ2_N0NJylmmXQpEKCRNHPgUsuRJaXCMAZoCqBqurQAs4F8FLlA7mkTKkSy6xkueIDlUnNbpL749ydd98thqbYuNbbuLLo50KKPI83iFTvSK1UjYWxlWu80tGXuDXaWaxMrA-zLN4WBkJGwcOZIDIN_jYr1YZQTN8X5-zjkdXeheCxKnbebJXfF0CLw9eKk6-xP9ISh_0</recordid><startdate>20230901</startdate><enddate>20230901</enddate><creator>Jia, Youdong</creator><creator>Liu, Jianxiong</creator><creator>Li, Zhengfang</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0009-0008-1847-8359</orcidid></search><sort><creationdate>20230901</creationdate><title>Research on Magnetic Rollers for Recovering Non-Ferrous Metals from End-of-Life Vehicles Employing Machine Learning</title><author>Jia, Youdong ; Liu, Jianxiong ; Li, Zhengfang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c371t-6919e62815956678be11531eab10af9e62155615ba849d03aabeb7b38a54a79c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Aluminum</topic><topic>Artificial intelligence</topic><topic>Efficiency</topic><topic>Electrical conductivity</topic><topic>Iron compounds</topic><topic>Machine learning</topic><topic>Magnetic fields</topic><topic>Metals</topic><topic>Nonferrous metal industry</topic><topic>Numerical analysis</topic><topic>Optimization</topic><topic>Recycling</topic><topic>Scrap</topic><topic>Scrap metals</topic><topic>Simulation</topic><topic>Simulation methods</topic><topic>Sustainability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jia, Youdong</creatorcontrib><creatorcontrib>Liu, Jianxiong</creatorcontrib><creatorcontrib>Li, Zhengfang</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jia, Youdong</au><au>Liu, Jianxiong</au><au>Li, Zhengfang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Research on Magnetic Rollers for Recovering Non-Ferrous Metals from End-of-Life Vehicles Employing Machine Learning</atitle><jtitle>Sustainability</jtitle><date>2023-09-01</date><risdate>2023</risdate><volume>15</volume><issue>18</issue><spage>13451</spage><pages>13451-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>Recovering copper foil and crushed aluminum from end-of-life vehicles (ELVs) is a significant issue in the recycling industry. As a key technology for sorting aluminum, copper, and other non-ferrous metals, eddy current separation (ECS) is efficient in isolating the non-ferrous metals according to their different electrical conductivity and density. However, further research is still needed in the separation of large-size copper foil and crushed aluminum from scrapped vehicles. In this study, support vector regression (SVR) and the sparrow search algorithm (SSA) are exploited for the first time to be used in optimizing the Halbach magnetic roller. Firstly, the numerical simulation results are based on the response surface methodology (RSM). Then, the accuracy of four kernel functions employing SVR is compared to select a kernel function. The sparrow search algorithm (SSA) is proposed to optimize the structural parameters of the Halbach magnetic roller, concentrating on the above-selected kernel function. Meanwhile, the parameters are confirmed. 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| subjects | Algorithms Aluminum Artificial intelligence Efficiency Electrical conductivity Iron compounds Machine learning Magnetic fields Metals Nonferrous metal industry Numerical analysis Optimization Recycling Scrap Scrap metals Simulation Simulation methods Sustainability |
| title | Research on Magnetic Rollers for Recovering Non-Ferrous Metals from End-of-Life Vehicles Employing Machine Learning |
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