SHAUN GRAHAM, Application specialist, Carl Zeiss Microscopy
Technological solution for the Polymetal challenge: Big data analytics to drive productivity optimisation
Despite the predictability of the mining boom and bust cycle, mining productivity has declined over the past decade. Digging it out fast when commodity prices are high has not protected miners from drops in the market. Ore grades are declining; transport distances from the face are increasing as mines mature and ore body replacement rates are dropping. As a result, miners are now looking at the implementation of big data analytics to their environment to improve productivity and efficiency throughout the entire process chain. A communication hive throughout the mine, between automated systems and operators, can speed up production changes by making decentralised decisions based on automated data gathering and analysis. In the short interval, an interconnection of computing devices (IOT) will allow information transfer backward or forward along the line to implement data-driven optimisation. In the longer interval, automated mineralogy data will feedback to the block model and so understanding of the resource is continuously refined and efficiency is continuously improved.