Development of a geometallurgical framework for process simulation coupled with automated mineralogy data

摘要

The minerals industry needs a quick, reliable and powerful predictive tool to perform techno-economic and sustainability evaluation of the raw materials value chain, being able to compare different process flowsheets and adapt to the ever increasing ore variability and complexity. This study makes use of quantitative mineralogical data, such as MLA (Mineral Liberation Analysis), and simulation tools (HSC Sim 9.6.1) to develop and test a framework for predictive geometallurgical tool using particle information. A particle-based process simulation takes into account a wider range of geometallurgical variables, such as mineral liberation by surface area, liberation by composition, particle information by size, particle density and shape to track particles and predict metallurgical response. Flotation unit models, however, should be designed to address particles rather than minerals, identifying which variables are most relevant to the separation process and assigning probability distribution to each particle reporting to concentrate or tailings.