Ghorbani, Y., Zhang, S. E., Nwaila, G. T., Bourdeau, J. E., Safari, M., Hadi Hoseinie, S., Nwaila, P., & Ruuska, J. (2023). Dry laboratories – Mapping the required instrumentation and infrastructure for online monitoring, analysis, and characterization in the mineral industry. In Minerals Engineering (Vol. 191, p. 107971). Elsevier BV. https://doi.org/10.1016/j.mineng.2022.107971
Dry laboratories : mapping the required instrumentation and infrastructure for online monitoring, analysis, and characterization in the mineral industry
|Author:||Ghorbani, Yousef1,2; Zhang, Steven E.3,4; Nwaila, Glen T.4,5;|
1Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, SE-97187 Luleå, Sweden
2School of Chemistry, University of Lincoln, Lincoln LN6 7TS, UK
3Geological Survey of Canada, 601 Booth Street, Ottawa, Ontario K1A 0E8, Canada
4Wits Mining Institute, University of the Witwatersrand, Private Bag 3, Wits 2050, South Africa
5School of Geosciences, University of the Witwatersrand, Private Bag 3, Wits 2050, South Africa
6Minerals Processing Division, Mintek, Private Bag X3015, Randburg 2125, South Africa
7Department of Mining Engineering, Isfahan University of Technology, Isfahan, Iran
8PG Techno Wox (Pty) Limited, 39 Kiewiet Street, Helikon Park, 1759, South Africa
9Faculty of Technology, Control Engineering, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 5.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20230825108166
|Publish Date:|| 2023-08-25
Dry laboratories (dry labs) are laboratories dedicated to using and creating data (they are data-centric). Several aspects of the minerals industry (e.g., exploration, extraction and beneficiation) generate multi-scale and multivariate data that are ultimately used to make decisions. Dry labs and digitalization are closely and intricately linked in the minerals industry. This paper focuses on the instrumentation and infrastructure that are required for accelerating digital transformation initiatives in the minerals sector. Specifically, we are interested in the ability of current and emerging instrumentation, sensors and infrastructure to capture relevant information, generate and transport high-quality data. We provide an essential examination of existing literature and an understanding of the 21st century minerals industry. Critical analysis of the literature and review of the current configuration of the minerals industry revealed similar data management and infrastructure needs for all segments of the minerals industry. There are, however, differences in the tools and equipment used at different stages of the mineral value chain. As demand for data-driven approaches grows, and as data resulting from each segment of the minerals industry continues to increase in abundance, diversity and dimensionality, the tools that manage and utilize such data should evolve in a way that is more transdisciplinary (e.g., data management, artificial intelligence, machine learning and data science). Ideally, data should be managed in a dry lab environment, but minerals industry data is currently and historically disaggregated. Consequently, digitalization in the minerals industry must be coupled with dry laboratories through a systematic transition. Sustained generation of high-quality data is critical to sustain the highly desirable uses of data, such as artificial intelligence-based insight generation.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
113 Computer and information sciences
The financial support of the Centre for Advanced Mining and Metallurgy (CAMM), a strategic research environment established at Luleå University of Technology funded by the Swedish government, is gratefully acknowledged.
© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).