Repurposing legacy metallurgical data Part I: A move toward dry laboratories and data bank

Advancements 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.

• Tipo de recurso: Article of investigation
• Fecha de publicación: 2020
• Institución: Universidad de Bogotá Jorge Tadeo Lozano
• Repositorio: Expeditio: repositorio UTadeo
• Idioma: eng
• OAI Identifier: oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/14249
• Acceso en línea: https://doi.org/10.1016/j.mineng.2020.106646; http://hdl.handle.net/20.500.12010/14249
• Palabra clave: Data analytics; Dry laboratories; Data bank; Legacy metallurgical data; Síndrome respiratorio agudo grave; COVID-19; SARS-CoV-2; Coronavirus
• License: Abierto (Texto Completo)