Geostatistics-block-based characterization of the relationship between rock mass quality and powder factor and its application on open-pit limit optimization

Accurately predicting the powder factor during blasting is essential for sustainable production planning in low-grade mines. This research presents a method for predicting powder factor based on the heterogeneity of rock mass rating (RMR). Considering a low-grade metal mine as an example, this study exploited geostatistical methods to obtain independent RMR for each block unit. A three-dimensional spatial distribution model for the powder factor was developed on the basis of the relationships between the RMR and the powder factor. Subsequently, models for blasting cost and mining value were built and employed to optimize the open-pit limit. The multi-variable model based on the RMR performed well in predicting the powder factor, achieving a correlation coefficient of 0.88 (root mean square error of 4.3) and considerably outperforming the uniaxial compressive strength model. After model optimization, the mean size and standard deviation of the fragments in the blast pile decreased by 8.5% and 35.1%, respectively, whereas the boulder yield and its standard deviation decreased by 33.3% and 58.8%, respectively. Additionally, optimizing the open-pit limit using this method reduced the amount of rock, increased the amount of ore, and lowered blasting costs, thereby enhancing the economic efficiency of the mine. This study provides valuable insights for blasting design and mining decisions, demonstrating the advantages and potential applications of powder factor prediction based on the heterogeneity of rock mass quality.