Intelligent optimization of bioleaching process for waste lithium‐ion batteries: An application of support vector regression approach
Summary Recovery of toxic and vital metal from spent Li‐ion batteries is a vital problem in the recycling industry. The recycling processes such as bioleaching are much simpler and environment friendly but lack the required efficiency for metal recovery to prove the commercial feasibility of the mod...
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Veröffentlicht in: | International journal of energy research 2021-03, Vol.45 (4), p.6152-6162 |
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creator | Ruhatiya, Chaitanya Gandra, Ruthvik Kondaiah, P Manivas, Kura Samhith, Aditya Gao, Liang Lam, Jasmine Siu Lee Garg, Akhil |
description | Summary
Recovery of toxic and vital metal from spent Li‐ion batteries is a vital problem in the recycling industry. The recycling processes such as bioleaching are much simpler and environment friendly but lack the required efficiency for metal recovery to prove the commercial feasibility of the model. This work focuses on increasing the efficiency of the bioleaching process by targeting its intermediate processes for maximum vital metal recovery. The intermediate process of biomass generation from Aspergillus niger fungus is targeted. The data from experimental design is modelled using support vector regression with v‐fold cross‐validation. The bioleaching process is optimized such that maximum biomass concentration is obtained for efficient and commercially viable metal recovery. The results are divided into four sections, each addressing an important issue of the recycling process mechanism. The generated model is found to have good stability and accurate process mechanism predictability. Global sensitivity and interaction analysis is employed for efficient weighted optimization. The model generated trends and optimization results are verified through the profiling curve as well as past literature experimental data. This work reports the maximum biomass concentration of 25 g/L. The model employed is stable and efficient, reaching a stable optimized value under 300 iterations. The optimized input parameters values obtained are 144.39 g/L, 1.29% v/v, 6.70, 1513.05 ppm, 4989.79 ppm, 2094.22 ppm, 347.57 ppm and 2.37 for sucrose concentration, inoculum size, initial pH, oxalic acid, gluconic acid, malic acid, citric acid concentration and final pH, respectively. |
doi_str_mv | 10.1002/er.6238 |
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Recovery of toxic and vital metal from spent Li‐ion batteries is a vital problem in the recycling industry. The recycling processes such as bioleaching are much simpler and environment friendly but lack the required efficiency for metal recovery to prove the commercial feasibility of the model. This work focuses on increasing the efficiency of the bioleaching process by targeting its intermediate processes for maximum vital metal recovery. The intermediate process of biomass generation from Aspergillus niger fungus is targeted. The data from experimental design is modelled using support vector regression with v‐fold cross‐validation. The bioleaching process is optimized such that maximum biomass concentration is obtained for efficient and commercially viable metal recovery. The results are divided into four sections, each addressing an important issue of the recycling process mechanism. The generated model is found to have good stability and accurate process mechanism predictability. Global sensitivity and interaction analysis is employed for efficient weighted optimization. The model generated trends and optimization results are verified through the profiling curve as well as past literature experimental data. This work reports the maximum biomass concentration of 25 g/L. The model employed is stable and efficient, reaching a stable optimized value under 300 iterations. The optimized input parameters values obtained are 144.39 g/L, 1.29% v/v, 6.70, 1513.05 ppm, 4989.79 ppm, 2094.22 ppm, 347.57 ppm and 2.37 for sucrose concentration, inoculum size, initial pH, oxalic acid, gluconic acid, malic acid, citric acid concentration and final pH, respectively.</description><identifier>ISSN: 0363-907X</identifier><identifier>EISSN: 1099-114X</identifier><identifier>DOI: 10.1002/er.6238</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Inc</publisher><subject>Acids ; Bacterial leaching ; Batteries ; bioleaching ; Biomass ; Citric acid ; Design of experiments ; Experimental design ; Feasibility studies ; Fungi ; Gluconic acid ; Inoculum ; intelligent optimization ; Leaching ; Lithium ; Lithium-ion batteries ; Malic acid ; Materials recovery ; Metals ; Optimization ; Oxalic acid ; pH effects ; Recovery ; Recycling ; Regression analysis ; spent batteries ; Stability ; Stability analysis ; Sucrose ; Sugar ; Support vector machines ; support vector regression ; waste management</subject><ispartof>International journal of energy research, 2021-03, Vol.45 (4), p.6152-6162</ispartof><rights>2020 John Wiley & Sons Ltd</rights><rights>2021 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3808-6953a482d3893904e139d8f056c4e4e30644617a5a8aebabd0cb68cbc63555ab3</citedby><cites>FETCH-LOGICAL-c3808-6953a482d3893904e139d8f056c4e4e30644617a5a8aebabd0cb68cbc63555ab3</cites><orcidid>0000-0003-3511-3577 ; 0000-0002-1485-0722 ; 0000-0002-4825-1226 ; 0000-0001-7920-2665</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fer.6238$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fer.6238$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Ruhatiya, Chaitanya</creatorcontrib><creatorcontrib>Gandra, Ruthvik</creatorcontrib><creatorcontrib>Kondaiah, P</creatorcontrib><creatorcontrib>Manivas, Kura</creatorcontrib><creatorcontrib>Samhith, Aditya</creatorcontrib><creatorcontrib>Gao, Liang</creatorcontrib><creatorcontrib>Lam, Jasmine Siu Lee</creatorcontrib><creatorcontrib>Garg, Akhil</creatorcontrib><title>Intelligent optimization of bioleaching process for waste lithium‐ion batteries: An application of support vector regression approach</title><title>International journal of energy research</title><description>Summary
Recovery of toxic and vital metal from spent Li‐ion batteries is a vital problem in the recycling industry. The recycling processes such as bioleaching are much simpler and environment friendly but lack the required efficiency for metal recovery to prove the commercial feasibility of the model. This work focuses on increasing the efficiency of the bioleaching process by targeting its intermediate processes for maximum vital metal recovery. The intermediate process of biomass generation from Aspergillus niger fungus is targeted. The data from experimental design is modelled using support vector regression with v‐fold cross‐validation. The bioleaching process is optimized such that maximum biomass concentration is obtained for efficient and commercially viable metal recovery. The results are divided into four sections, each addressing an important issue of the recycling process mechanism. The generated model is found to have good stability and accurate process mechanism predictability. Global sensitivity and interaction analysis is employed for efficient weighted optimization. The model generated trends and optimization results are verified through the profiling curve as well as past literature experimental data. This work reports the maximum biomass concentration of 25 g/L. The model employed is stable and efficient, reaching a stable optimized value under 300 iterations. The optimized input parameters values obtained are 144.39 g/L, 1.29% v/v, 6.70, 1513.05 ppm, 4989.79 ppm, 2094.22 ppm, 347.57 ppm and 2.37 for sucrose concentration, inoculum size, initial pH, oxalic acid, gluconic acid, malic acid, citric acid concentration and final pH, respectively.</description><subject>Acids</subject><subject>Bacterial leaching</subject><subject>Batteries</subject><subject>bioleaching</subject><subject>Biomass</subject><subject>Citric acid</subject><subject>Design of experiments</subject><subject>Experimental design</subject><subject>Feasibility studies</subject><subject>Fungi</subject><subject>Gluconic acid</subject><subject>Inoculum</subject><subject>intelligent optimization</subject><subject>Leaching</subject><subject>Lithium</subject><subject>Lithium-ion batteries</subject><subject>Malic acid</subject><subject>Materials recovery</subject><subject>Metals</subject><subject>Optimization</subject><subject>Oxalic acid</subject><subject>pH effects</subject><subject>Recovery</subject><subject>Recycling</subject><subject>Regression analysis</subject><subject>spent batteries</subject><subject>Stability</subject><subject>Stability analysis</subject><subject>Sucrose</subject><subject>Sugar</subject><subject>Support vector machines</subject><subject>support vector regression</subject><subject>waste management</subject><issn>0363-907X</issn><issn>1099-114X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp10EFLwzAYBuAgCs4p_oWABw_SmTRtlnobY-pgIIjCbiXNvm4ZXVOT1DFP3rz6G_0lpk68ecrhe_K-8CJ0TsmAEhJfgx3wmIkD1KMkyyJKk_kh6hHGWZSR4fwYnTi3JiTc6LCHPqa1h6rSS6g9No3XG_0mvTY1NiUutKlAqpWul7ixRoFzuDQWb6XzgCvtV7rdfL1_dryQ3oPV4G7wqMayaSqt_oJc2zTGevwKyof_FpY2ZHXHAK0JFafoqJSVg7Pft4-ebydP4_to9nA3HY9mkWKCiIhnKZOJiBdMZCwjCVCWLURJUq4SSIARniScDmUqhYRCFguiCi5UoThL01QWrI8u9rmh9qUF5_O1aW0dKvM4JVTwsFEa1OVeKWucs1DmjdUbaXc5JXm3cg4271YO8movt7qC3X8snzz-6G9XC4Ej</recordid><startdate>20210325</startdate><enddate>20210325</enddate><creator>Ruhatiya, Chaitanya</creator><creator>Gandra, Ruthvik</creator><creator>Kondaiah, P</creator><creator>Manivas, Kura</creator><creator>Samhith, Aditya</creator><creator>Gao, Liang</creator><creator>Lam, Jasmine Siu Lee</creator><creator>Garg, Akhil</creator><general>John Wiley & Sons, Inc</general><general>Hindawi Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7ST</scope><scope>7TB</scope><scope>7TN</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>F28</scope><scope>FR3</scope><scope>H96</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0003-3511-3577</orcidid><orcidid>https://orcid.org/0000-0002-1485-0722</orcidid><orcidid>https://orcid.org/0000-0002-4825-1226</orcidid><orcidid>https://orcid.org/0000-0001-7920-2665</orcidid></search><sort><creationdate>20210325</creationdate><title>Intelligent optimization of bioleaching process for waste lithium‐ion batteries: An application of support vector regression approach</title><author>Ruhatiya, Chaitanya ; Gandra, Ruthvik ; Kondaiah, P ; Manivas, Kura ; Samhith, Aditya ; Gao, Liang ; Lam, Jasmine Siu Lee ; Garg, Akhil</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3808-6953a482d3893904e139d8f056c4e4e30644617a5a8aebabd0cb68cbc63555ab3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Acids</topic><topic>Bacterial leaching</topic><topic>Batteries</topic><topic>bioleaching</topic><topic>Biomass</topic><topic>Citric acid</topic><topic>Design of experiments</topic><topic>Experimental design</topic><topic>Feasibility studies</topic><topic>Fungi</topic><topic>Gluconic acid</topic><topic>Inoculum</topic><topic>intelligent optimization</topic><topic>Leaching</topic><topic>Lithium</topic><topic>Lithium-ion batteries</topic><topic>Malic acid</topic><topic>Materials recovery</topic><topic>Metals</topic><topic>Optimization</topic><topic>Oxalic acid</topic><topic>pH effects</topic><topic>Recovery</topic><topic>Recycling</topic><topic>Regression analysis</topic><topic>spent batteries</topic><topic>Stability</topic><topic>Stability analysis</topic><topic>Sucrose</topic><topic>Sugar</topic><topic>Support vector machines</topic><topic>support vector regression</topic><topic>waste management</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ruhatiya, Chaitanya</creatorcontrib><creatorcontrib>Gandra, Ruthvik</creatorcontrib><creatorcontrib>Kondaiah, P</creatorcontrib><creatorcontrib>Manivas, Kura</creatorcontrib><creatorcontrib>Samhith, Aditya</creatorcontrib><creatorcontrib>Gao, Liang</creatorcontrib><creatorcontrib>Lam, Jasmine Siu Lee</creatorcontrib><creatorcontrib>Garg, Akhil</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><jtitle>International journal of energy research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ruhatiya, Chaitanya</au><au>Gandra, Ruthvik</au><au>Kondaiah, P</au><au>Manivas, Kura</au><au>Samhith, Aditya</au><au>Gao, Liang</au><au>Lam, Jasmine Siu Lee</au><au>Garg, Akhil</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Intelligent optimization of bioleaching process for waste lithium‐ion batteries: An application of support vector regression approach</atitle><jtitle>International journal of energy research</jtitle><date>2021-03-25</date><risdate>2021</risdate><volume>45</volume><issue>4</issue><spage>6152</spage><epage>6162</epage><pages>6152-6162</pages><issn>0363-907X</issn><eissn>1099-114X</eissn><abstract>Summary
Recovery of toxic and vital metal from spent Li‐ion batteries is a vital problem in the recycling industry. The recycling processes such as bioleaching are much simpler and environment friendly but lack the required efficiency for metal recovery to prove the commercial feasibility of the model. This work focuses on increasing the efficiency of the bioleaching process by targeting its intermediate processes for maximum vital metal recovery. The intermediate process of biomass generation from Aspergillus niger fungus is targeted. The data from experimental design is modelled using support vector regression with v‐fold cross‐validation. The bioleaching process is optimized such that maximum biomass concentration is obtained for efficient and commercially viable metal recovery. The results are divided into four sections, each addressing an important issue of the recycling process mechanism. The generated model is found to have good stability and accurate process mechanism predictability. Global sensitivity and interaction analysis is employed for efficient weighted optimization. The model generated trends and optimization results are verified through the profiling curve as well as past literature experimental data. This work reports the maximum biomass concentration of 25 g/L. The model employed is stable and efficient, reaching a stable optimized value under 300 iterations. The optimized input parameters values obtained are 144.39 g/L, 1.29% v/v, 6.70, 1513.05 ppm, 4989.79 ppm, 2094.22 ppm, 347.57 ppm and 2.37 for sucrose concentration, inoculum size, initial pH, oxalic acid, gluconic acid, malic acid, citric acid concentration and final pH, respectively.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/er.6238</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-3511-3577</orcidid><orcidid>https://orcid.org/0000-0002-1485-0722</orcidid><orcidid>https://orcid.org/0000-0002-4825-1226</orcidid><orcidid>https://orcid.org/0000-0001-7920-2665</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Acids Bacterial leaching Batteries bioleaching Biomass Citric acid Design of experiments Experimental design Feasibility studies Fungi Gluconic acid Inoculum intelligent optimization Leaching Lithium Lithium-ion batteries Malic acid Materials recovery Metals Optimization Oxalic acid pH effects Recovery Recycling Regression analysis spent batteries Stability Stability analysis Sucrose Sugar Support vector machines support vector regression waste management |
title | Intelligent optimization of bioleaching process for waste lithium‐ion batteries: An application of support vector regression approach |
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