The mining rate determines the profitability of a mine and when set at an optimal rate, it generates maximum NPV. Although the mining rate depends on many parameters, ore tonnage has been used as the main determinant in developing empirical models for predicting the mining rate. However, the results obtained depend on the characteristics of the ore body, mine, and the number of samples of mines used. The main aim of this chapter is to develop regression models that can be used in predicting the mining rate in any gold mining in the pre-production stage given reserve variations and type of mines such as open pit or underground mines. The data used in this study include mining rate, reserve tonnage, and type of mine whether open pit, underground, or both open pit and underground. More than 165 gold mines/deposits obtained from the Raw Material Group database were used in the analysis and other 50 deposits were used for validation. Multiple Linear Regression Analysis using the method of enter was selected to develop regression models. The main results showed that the mining rate can be estimated based on the reserve tonnage with a value of R2 of 79.9%. Further analysis using ANN confirmed this result with an improved value of R2 of 80%. The validation of the regression models using other gold deposit data indicated an average error of 20%. This confirms a strong relationship between the mining rate and reserve tonnage.
Author(s) Details
Karim Rajabu Baruti
University of Dar es Salaam, Tanzania.
Please see the book here:- https://doi.org/10.9734/bpi/caert/v5/5842E
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