MWD Data-Based Marble Quality Class Prediction Models Using ML Algorithms

Abstract Brønnøy Kalk AS operates an open pit mine in Norway producing marble, mainly used by the paper industry. The final product is as filler and pigment for production. Therefore, quality of has utmost importance. In mine, primary indicator, called TAPPI, quantified through a laborious sampling process laboratory experiments. As part digital transformation, measurement while drilling (MWD) data have been collected mine. purpose this to use recorded MWD prediction marble facilitate blending pit. For purpose, two supervised classification modelling algorithms such conventional logistic regression random forest employed. results show that model presents significantly higher statistical performance, it can be tool fast efficient assessment.