Abstract
Mining operations record a large amount of data from multiple sources (such as block model and online processing data) which is neither effectively nor systematically used to understand and improve operational performance. This paper proposes a generic semi-automatable data analytics method, the Integrated Analysis Method (IAM), that addresses the disconnection between disparate datasets. IAM enables evidence-based understanding of rock and machine parameters, laying the foundation for a potentially more sophisticated way to model and predict mining processes to deliver financial value. IAM systematically combines and analyses both rock characteristics and operational data to isolate the impact of the variability in rock characteristics and operational settings on key performances. Insights extracted from IAM allow one to narrow down key operating conditions, specific to a particular plant, that are correlated to, for example, significant differences in daily throughput while processing batches of ore with similar metallurgical characteristics. Such insights can be used for multiple purposes, for instance, to learn optimal processing recipes for a given set of rock properties. We applied IAM to a combined data set recorded at a Chilean ore deposit and evaluated our findings with domain experts.
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