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dc.creatorGhorbani, Yousef
dc.creatorNwaila, Glen T.
dc.creatorZhang, Steven E.
dc.creatorHay, Martyn P.
dc.creatorBam, Lunga C.
dc.creatorIstiadi Guntoro, Pratama
dc.date.accessioned2020-10-06T16:22:00Z
dc.date.available2020-10-06T16:22:00Z
dc.date.created2020
dc.identifier.issn0892-6875spa
dc.identifier.otherhttps://doi.org/10.1016/j.mineng.2020.106646spa
dc.identifier.urihttp://hdl.handle.net/20.500.12010/14249
dc.description.abstractAdvancements in modern mineral processing has been driven by technology and fuelled by market economics of supply and demand. Over the last three decades, the demand for various minerals has steadily increased, while the mineral processing industry has seen an unavoidable increase in the treatment of complex ores, continuous decline in plant feed grade and poor plant performance partly due to blending of ores with dissimilar properties. Despite these challenges, production plant data that are routinely generated are usually underutilised. In this contribution and aligned with the direction of the 4th industrial revolution, we highlight the value of legacy metallurgical plant data and the concept of a dry laboratory approach. This study is presented in two parts. In the current paper (Part I), a comprehensive review of the potential for the combination of modern analytical technology with data analytics to generate a new competence for process optimisation are provided. To demonstrate the value of data within the extractive metallurgy discipline, we employ data analytics and simulation to examine gold plant performance and the flotation process in two separate case studies in the second paper (Part II). This was done with the aim of showcasing relevant plant data insights, and extract parameters that should be targeted for plant design and performance optimisation. We identify several promising technologies that integrate well with existing mineral processing plants and testing laboratories to exploit the concept of a dry laboratory, in order to enhance pre-existing mineral processing chains. It also sets the passage in terms of the value of innovative analysis of existing and simulation data as part of the new world of data analytics. Using data- and technology-driven initiatives, we propose the establishment of dry laboratories and data banks to ultimately leverage integrated data, analytics and process simulation for effective plant design and improved performance.spa
dc.format.extent11 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherMinerals Engineeringspa
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectData analyticsspa
dc.subjectDry laboratoriesspa
dc.subjectData bankspa
dc.subjectLegacy metallurgical dataspa
dc.titleRepurposing legacy metallurgical data Part I: A move toward dry laboratories and data bankspa
dc.type.localArtículospa
dc.subject.lembSíndrome respiratorio agudo gravespa
dc.subject.lembCOVID-19spa
dc.subject.lembSARS-CoV-2spa
dc.subject.lembCoronavirusspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.rights.localAbierto (Texto Completo)spa
dc.identifier.doihttps://doi.org/10.1016/j.mineng.2020.106646spa
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa


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