Dry laboratories � Mapping the required instrumentation and infrastructure for online monitoring, analysis, and characterization in the mineral industry

Ghorbani, Y., Zhang, S.E., Nwaila, G.T. , Bourdeau, J.E., Safari, M., Hadi Hoseinie, S., Nwaila, P. and Ruuska, J. (2023) Dry laboratories � Mapping the required instrumentation and infrastructure for online monitoring, analysis, and characterization in the mineral industry. Minerals Engineering, 191 . p. 107971. ISSN 0892-6875

Full content URL: https://doi.org/10.1016/j.mineng.2022.107971

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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. © 2022 The Author(s)

Keywords:Artificial intelligence, Information management, Laboratories, Mineral exploration, Mineral industry, Minerals, Data analytics, Data centric, Data driven, Data-centric/data-driven, Dry laboratory, High quality data, Instrumentation, Minerals industry, On-line characterization, 'Dry'
Divisions:College of Science
College of Science > School of Chemistry
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ID Code:54485
Deposited On:28 Jul 2023 12:13

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