The fact that the minerals processing sector needs digital transformation is a given. Many operators have tried. Some have succeeded, and many have failed. In this paper, the author discusses the distinct phases of digital transformation in the mining and mineral processing sector. Whilst discussing the defining characteristics of each phase, the author also highlights the importance of machine learning and artificial intelligence in optimising plant performance and that with the right data-led systems in place, return on investment can be rapid. The author also hypothesises that the industry currently under-achieves in digital transformation because most operators dont have the right systems in place and arent utilising data sufficiently or in the right way. When it comes to governance, the mineral processing industry is unique in the sense that there is currently no detailed or legislated framework for the reporting of plant performance. The author suggests that currently, the majority of plant reporting is done manually, using ad-hoc systems or processes. This poses a problem in terms of compliance, and also means that individual operators are missing a huge opportunity to optimise their plant performance, as well as to improve outputs and revenue. While engineers and metallurgists are well-accustomed to applying science and mathematics to develop economical solutions to technical problems, the modern plant needs to go beyond manual analysis. For true, lasting digital transformation to occur, there is a need for robust, powerful, AI-led technology that can provide operators with a reliable digital twin of their plant, and function as a single source of truth.
CITATION:Vagenas, J, 2019. The Stages of Digital Transformation, in Proceedings MetPlant 2019, pp 117118 (The Australasian Institute of Mining and Metallurgy: Melbourne).