Improving mine-to-mill by data warehousing and data mining
This study gives insight into a data-driven framework for modern mines and presents a data mining implementation on real-time mining-related data for prediction of blasting performance, using random forest and adaptive boosting algorithm on an integrated data warehouse.
Abstract
ABSTRACT Mining is an interdisciplinary industry that utilises equipment and technology intensively in daily operations. Mine-to-Mill is considered as a key concept for metal mining recently. Impact of underperformed basic upstream operations such as drilling and blasting will sustain this inefficiency in downstream processes, such as mineral processing. Data provided for each of these operations from software and hardware utilised on field reached a level where advanced data analytics becomes applicable. Data warehousing and data mining are alternative tools that rely on a robust data structure. This study gives insight into a data-driven framework for modern mines and presents a data mining implementation on real-time mining-related data for prediction of blasting performance. Random forest and adaptive boosting algorithm were utilised on an integrated data warehouse to discover major operational parameters for efficient blasting. The implementation on site improved the performance of drilling and blasting. The variables highlighted as important by random forest and adaptive boosting algorithm directed the experts of mine-to-mill on site to focus on the close control and detailed analysis of certain drilling- and blasting-related parameters.