A review of state-of-the-art techniques for the determination of the optimum cut-off grade of a metalliferous deposit with a bibliometric mapping in a surface mine planning context

Abstract

In terms of the global share, 95% of the non-metallic minerals, 90% of the metallic minerals, and about 60% of coal are being mined out through surface mining methods. In a mining operation, the grade-tonnage distribution of the deposit necessitates that not all of the material inside the open pit can be treated. Given this variability, it is critical to identify ore and waste elements correctly. The global population is expected to increase from 8 billion in 2022 to more than 9.7 billion in 2050. World metal consumption increases at around 3.2% per year, driving trade and economic diversification. Therefore, to guarantee a continuous supply of the minerals from the metalliferous surface mining industry in terms of techno-economic concerns, cut-off grade (COG) optimization is the key. The economic requirement aims towards the maximization of the return on investment, while techno-economic sustainability aims towards the maximization of resource recovery. Optimization of COG for surface mine design has come a long way in the last 60 years, primarily using analytical models based on the traditional methodology. In the past five years, non-conventional evolutionary algorithms have been extensively used. However, the analytical methods can be credited with the maximum amount of work, yet none can provide optimal outputs. This review article presents techniques, advancements, limitations, difficulties, bibliographic analysis, and potential future research paths in COG optimization for surface mine planning.