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 wit...