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dc.contributor.authorChang, Jiyoun Christina
dc.contributor.authorOlivetti, Elsa A.
dc.contributor.authorFjeldbo, Snorre Kjørstad
dc.contributor.authorKirchain, Randolph E.
dc.date.accessioned2016-06-27T16:37:43Z
dc.date.available2016-06-27T16:37:43Z
dc.date.issued2015-01
dc.identifier.issn2199-3823
dc.identifier.issn2199-3831
dc.identifier.urihttp://hdl.handle.net/1721.1/103356
dc.description.abstractRecycling provides a key strategy to move toward a more sustainable society by partially mitigating the impact of fast-growing material consumption. One main barrier to increased recycling arises from the fact that in many real world contexts, the quality of secondary (or scrap) material is unknown and highly variable. Even if scrap material is of known quality, there may be finite space or limited operational flexibility to separate or sort these materials prior to use. These issues around identification and grouping given the operational constraints create limitations to simply developing an appropriate sorting strategy, let alone implementing one. This study suggests the use of data mining as a strategy to manage raw materials with uncertain quality using existing data from the recycling industry. A clustering analysis is used to recognize the pattern of raw materials across a broad compositional range in order to provide criteria for grouping (binning) raw materials. This strategy is applied to an industrial case of aluminum recycling to explore the benefits and limitations in terms of secondary material usage. In particular, the case investigated is around recycling industrial byproducts (termed dross for the case of the aluminum industry). The binning strategy obtained by the clustering analysis can significantly reduce material cost by increasing the compositional homogeneity and distinctiveness of uncertain raw materials. This result suggests the potential opportunity to increase low-quality secondary raw material usage before investment in expensive sorting technology.en_US
dc.description.sponsorshipNorsk Hydroen_US
dc.publisherSpringer International Publishingen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s40831-014-0003-3en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceSpringer International Publishingen_US
dc.titleData Mining Toward Increased Use of Aluminum Drossen_US
dc.typeArticleen_US
dc.identifier.citationChang, Jiyoun, Elsa Olivetti, Snorre Kjørstad Fjeldbo, and Randolph Kirchain. "Data Mining Toward Increased Use of Aluminum Dross." Journal of Sustainable Metallurgy, March 2015, Volume 1, Issue 1, pp 53–64.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Materials Science and Engineeringen_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Divisionen_US
dc.contributor.mitauthorChang, Jiyoun Christinaen_US
dc.contributor.mitauthorOlivetti, Elsa A.en_US
dc.contributor.mitauthorKirchain, Randolph E.en_US
dc.relation.journalJournal of Sustainable Metallurgyen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2016-05-23T12:13:04Z
dc.language.rfc3066en
dc.rights.holderThe Minerals, Metals, & Materials Society (TMS)
dspace.orderedauthorsChang, Jiyoun; Olivetti, Elsa; Fjeldbo, Snorre Kjørstad; Kirchain, Randolphen_US
dspace.embargo.termsNen
dc.identifier.orcidhttps://orcid.org/0000-0002-3336-8472
dc.identifier.orcidhttps://orcid.org/0000-0002-8043-2385
mit.licenseOPEN_ACCESS_POLICYen_US


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