Bringing predictability into a geometallurgical program : An iron ore case study

• Main Author: Lishchuk, Viktor
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: LuleÃ¥ tekniska universitet, Mineralteknik och metallurgi 2019
• Subjects: Additivity; Apatite iron ore; AIO; Block model; Change of support; Classification; Data integration; DT; Feed quality; Geometallurgical program; Geometallurgy; Iron ore; Iron recovery; Leveäniemi; Liberation; Machine learning; Magnetic separation; Malmberget; Mineralogical approach; Mineralogy; Prediction; Proxies; Proxies approach; Sampling; Simulation; Synthetic ore body; Traditional approach; WLIMS; Mineral and Mine Engineering; Mineral- och gruvteknik; Metallurgy and Metallic Materials; Metallurgi och metalliska material; Niemi
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71580

The risks of starting, operating and closing mining projects have become higher than ever. In order to stay ahead of the competition, mining companies have to manage various risks: technical, environmental, legal, regulatory, political, cyber, financial and social. Some of these can be mitigated with the help of geometallurgy. Geometallurgy aims to link geological variability with responses in the beneficiation process by a wide usage of automated mineralogy, proxy metallurgical tests, and process simulation. However, traditional geometallurgy has neglected the non-technical aspects of mining. This has caused wide-spread discussion among researchers on the benefits of geometallurgy and its place in industry. In order to improve predictability in geometallurgy, such programs should cover planning, and the testing of hypotheses, and only then should there be an attempt to develop suitable technical tools. Such approach would ensure that those tools would be useful and are needed, not only from the technical point of view, but also from the users’ perspective. Therefore, this thesis introduces methodology on how to decrease uncertainty in the production planning and thus determine how much effort to put into decreasing uncertainty in geometallurgical programs. The predictability improvement of a geometallurgical program starts at the planning stage. The classification system developed here, through the survey (interviews) and literature review, indicates different ways to link geological information with metallurgical responses, and suggests areas where technical development is called for. The proposed developments can be tested before the start of the geometallurgical program with synthetic data. For the iron ore reference study (Malmberget), it was shown that implementation of geometallurgy is beneficial in terms of net present value (NPV) and internal rate of return (IRR), and building geometallurgical spatial model for the process properties (recovery and total concentrate tonnages), and that it requires ...