Repurposing legacy metallurgical data part II: Case studies of plant performance optimisation and process simulation

Ghorbani, Y., Nwaila, G.T., Zhang, S.E. and Hay, M.P. (2021) Repurposing legacy metallurgical data part II: Case studies of plant performance optimisation and process simulation. Minerals Engineering, 160 . p. 106667. ISSN 0892-6875

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The history of the metallurgical industry is rich with data. An enormous amount of data is generated from mining operations and industrial factories, and as deployment of new technologies such as on-line monitoring and in-situ instrumentation proliferate through the 4th industrial revolution, the quantity and quality of data will increase dramatically. The first paper (Part I), describes a range of promising technologies that integrate well with existing mineral processing plants and testing laboratories to demonstrate the enormous potential of a dry laboratory. A dry lab is a type of laboratory that includes applied or computational mathematical analyses for an extensive range of different applications. In both laboratories and mineral processing plants, integration of timely, accurate and reliable data analytics is key to leveraging data to enable data-driven plant design, optimisation and monitoring. However and despite progresses in analytical technology and increasing availability of data and sophisticated data analytics, legacy metallurgical plant and test work data are being underutilised. Understanding the insights contained within legacy metallurgical plant data is critical to the transition into a data- and analytics-driven industry. This paper (Part II) details two case studies that use legacy data to benefit metallurgical processes. One case study focuses on operational data from a gold recovery plant and provide indirect knowledge of the structure and/or composition of the feed sources, and insights to guide the optimisation of the operation. The other case study focuses on laboratory flotation tests, and demonstrates the effectiveness of aggregated data in establishing empirical guidelines that can guide the design and optimisation of new and existing processing operations. © 2020 The Author(s)

Keywords:Data Analytics, Flotation, Metallurgy, Metals, Industrial revolutions, Mathematical analysis, Metallurgical industry, Metallurgical process, Mineral processing plants, Processing operations, Well testing, Analytical technology, Laboratories
Divisions:College of Science
College of Science > School of Chemistry
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ID Code:54510
Deposited On:31 Jul 2023 10:23

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