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Based on the data of global mineral production and ore recovery in mining and mineral processing, this paper presents the annual production of 25 minerals and calculates the annual losses of 20 minerals from the year 1920 to 2018. The results indicate that the annual production of 23 minerals has increased by between 1 and 930 times since 1920, similar to the increase in the GDP per capita since 1960. At the same time, a vast amount of minerals has been lost in mining and mineral processing since 1920. Hence, this paper discusses the possibility of increasing ore recovery ratio.

In this study, the application of characteristic impedance in estimating specific energy and average fragment size of rocks was investigated during rock breakage at high strain rates. To achieve this, rock specimen was prepared in accordance with recommendations of the International Society for Rock Mechanics and broken at high strain rates using the split Hopkinson’s pressure bar system. Results reveal that although strain rate is well related to specific energy and average fragment size of broken rocks, the product of characteristic impedance and strain rate is more reliable for estimating the forementioned parameters. In addition, strain rate and dissipated energy generally increase at higher incident energies while the average fragment size of broken rocks reduces at higher strain rates. Based on these findings, more studies on indirect estimation of energy requirement for rock breakage to desired average fragment sizes is recommended from the product of characteristic impedance and strain rate.

Rock properties are important for design of surface and underground mines as well as civil engineering projects. Among important rock properties is the characteristic impedance of rock. Characteristic impedance plays a crucial role in solving problems of shock waves in mining engineering. The characteristics impedance of rock has been related with other rock properties in literature. However, the regression models between characteristic impedance and other rock properties in literature do not consider the variabilities in rock properties and their characterizations. Therefore, this study proposed two soft computing models [i.e., artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)] for better predictions of characteristic impedance of igneous rocks. The performances of the proposed models were statistically evaluated, and they were found to satisfactorily predict characteristic impedance with very strong statistical indices. In addition, multiple linear regression (MLR) was developed and compared with the ANN and ANFIS models. ANN model has the best performance, followed by ANFIS model and lastly MLR model. The models have Pearson's correlation coefficients of close to 1, indicating that the proposed models can be used to predict characteristic impedance of igneous rocks.

Deformation modulus of rock mass (Em) is an important parameter for the analysis and design of mining engineering projects. However, field tests for measuring deformation modulus of rock mass are difficult, time-consuming, and capital intensive. This has led to the development of numerous empirical models for estimating rock mass deformation modulus, which are in different forms and scattered in the literature. The numerous models available in the literature use different types of inputs. Therefore, this study provides a comprehensive compilation of different empirical models for estimating the deformation modulus of rock masses. The compiled models are grouped based on their type of input parameter(s) into three categories such as those using intact rock properties, rock mass classification indices, and combination of intact rock properties and rock mass classification indices. Then, a comparative analysis was performed using absolute average relative error percentage (AAREP) and variance accounted for (VAF) to assess the reliability of using different types of inputs for estimation of deformation modulus of rock masses using data from two sites. The results of the analyses show that rock mass classification indices are the most reliable indices for estimating the deformation modulus of rock masses among the categories considered for analyses. For AAREP analyses in the two illustrative examples considered in this study, models (7 out of 10) using rock mass classification indices in the estimation of Em have the best performances with AAREP values ranging from 24.07 to 55.15%. For VAF analyses in the two examples, models (8 out of 10) using rock mass classification indices in the estimation of Em have the best performances with values ranging from 59.81 to 88.11%. The lowest errors and highest deviation similarities from models using rock mass classification indices indicate that they produce the most reliable estimations of Em. It is important to note that the reliability of deformation modulus estimated from empirical models depends on the quality of input data as the models performed differently across the sites used in this study. This study therefore provides a compilation of available models for estimating deformation modulus, performance evaluation of available models for estimating deformation modulus, and guidelines for selecting appropriate model for estimating deformation modulus of rock mass.