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dc.contributor.advisorTaylor, Patrick R.
dc.contributor.advisorSeetharaman, Sridhar
dc.contributor.authorGao, Zhijiang
dc.date.accessioned2021-09-13T10:21:09Z
dc.date.accessioned2022-02-03T13:23:54Z
dc.date.available2021-09-13T10:21:09Z
dc.date.available2022-02-03T13:23:54Z
dc.date.issued2021
dc.identifierGao_mines_0052E_12201.pdf
dc.identifierT 9161
dc.identifier.urihttps://hdl.handle.net/11124/176512
dc.descriptionIncludes bibliographical references.
dc.description2021 Summer.
dc.description.abstractSteel scrap, especially for automobile scrap, being the most recycled material in the world, is facing the problem of limited consumption due to the contamination of impurities, such as Cu, Sn and Zn. Among these impurities, Cu could be the priority area of concern, which could be classified as isolated Cu impurities and alloyed Cu, leading to the inducing of surface hot shortness during hot working due to high Cu content (above 0.1wt%). To eliminate this issue and develop a circular economy with energy efficiency, various removal technologies have been researched. With the access to actual automobile scrap and following characterization, this research is aimed at carrying out a fundamental study to pursue feasible methods, including physical separation and chemical removal, to overcome the existed technical gaps. For physical separation, considering the distinct color difference between metal Cu and Fe, optical recognition was explored as a candidate for sorting Cu impurities with the improvement of machine learning. With further research of hyperparameter optimization, Cu recognizing accuracy of 87.5% was achieved, resulting in overall reduction in Cu content from 0.272wt% to 0.093wt%, indicating good feasibility to reduce Cu content to the 0.1wt% limitation. For blue laser sensor, as another candidate, initial laboratory tests conducted with pure Cu and Fe sheets have confirmed its sorting mechanism related to the difference of thermal conductivity and absorption. But the accessibility of industrial blue laser source could be a limitation for conducting further laboratory experiments with actual automobile scrap. For chemical removal, chlorination with Cl2-O2 gas at 800 oC and slagging method with FeO-SiO2-CaCl2 at 1600 oC have been identified and focused for initial experiments. For chlorination method, it demonstrates good feasibility to remove isolated Cu impurities, while potential for removing alloyed Cu in steel scrap could still need to be discussed. For slagging method, it demonstrates good feasibility to remove Cu impurities. Then mathematic equations have been established based on the chemical equilibria and mass balance of chemical reaction to project Cu concentration in molten steel with the application of this technology to build the kinetic understanding.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartof2021 - Mines Theses & Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectmachine learning
dc.subjectslag-metal reaction
dc.subjectsurface hot shortness
dc.subjectoptical recognition
dc.subjectCu impurities
dc.subjectsteel scrap
dc.titleSorting and impurity removal to improve the recycling of steel scrap from auto shredders
dc.typeText
dc.contributor.committeememberSpiller, D. Erik
dc.contributor.committeememberAnderson, Corby G.
dc.contributor.committeememberEggert, Roderick G.
thesis.degree.nameDoctor of Philosophy (Ph.D.)
thesis.degree.levelDoctoral
thesis.degree.disciplineMetallurgical and Materials Engineering
thesis.degree.grantorColorado School of Mines


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