A Machine Vision- and AI-Based Solution for Optimal Comminution in Mineral Processing Circuits

Comminution, the process of reducing particle size so that valuable minerals can be liberated from the ore, consumes most of the energy used in mining operations. This process consumes an estimated four percent of the world’s electrical power and accounts for 50% of a mine site’s overall power consumption. Although mine-to-mill optimization strategies have been discussed for the past three decades, they have had little overall impact on the industry. The proposed research aims to develop a post-blast solution that reduces energy consumption during the size reduction process through optimization of crushing by measuring input and output particle size, autonomously adjusting the crusher gap settings, and improving downstream processes. Also, by continually monitoring the ore content (particle size and mineralogy) at various stages in the comminution circuit, the proposed approach will eliminate low-grade waste from the energy-intensive processing stream. Beyond the significant positive impact on the profitability of mining operations, reducing energy consumption and eliminating unintended waste processing at mines can have many environmental benefits such as reduced reliance on fossil fuels and reduced freshwater consumption and mining rates.

Intern: 
Nursultan Iliyas;Obada Alhumsi
Faculty Supervisor: 
Ilija Miskovic;Sanja Miskovic;Bern Klein
Province: 
British Columbia
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