Abstract
Due to the complex geology of vein deposits and their erratic grade distributions, there is the tendency of overestimating or underestimating the ore grade. These estimated grade results determine the profitability of mining the ore deposit or otherwise. In this study, five Extreme Learning Machine (ELM) variants based on hard limit, sigmoid, triangular basis, sine and radial basis activation functions were applied to predict ore grade. The motive is that the activation function has been identified to play a key role in achieving optimum ELM performance. Therefore, assessing the extent of influence the activation functions will have on the final outputs from the ELM has some scientific value worth investigating. This study therefore applied ELM as ore grade estimator which is yet to be explored in the literature. The obtained results from the five ELM variants were analysed and compared with the state-of-the-art benchmark methods of Backpropagation Neural Network (BPNN) and Ordinary Kriging (OK). The statistical test results revealed that the ELM with sigmoid activation function (ELM-Sigmoid) was the best among all the other investigated methods (ELM-Hard limit, ELM-Triangular basis, ELM-Sine, ELM-Radial Basis, BPNN and OK). This is because the ELM-sigmoid produced the lowest MAE (0.0175), MSE (0.0005) and RMSE (0.0229) with highest R2 (91.93%) and R (95.88%) respectively. It was concluded that ELM-Sigmoid can be used by field practitioners as a reliable alternative ore grade estimation technique.
Highlights
A n important aspect of mining is ore grade estimation, since it determines the viability of actively mining a mineral of interest
Extreme Learning Machine (ELM) model for ore grade estimation Based on the experimental results, the optimum number of neurons for the developed ELM model was 50
Five variants of the ELM based on triangular basis, radial basis, hard limit, sine and sigmoid activation functions were developed and tested with data from a mine in Ghana
Summary
A n important aspect of mining is ore grade estimation, since it determines the viability of actively mining a mineral of interest. Manual tasking during the geostatistical resource estimation processes encourages bias and may introduce errors in the predicted ore grade values. These practical limitations are found in the most widely used geostatistical technique of Ordinary Kriging (OK). In the quest to fix and improve the performance of OK, various kriging techniques such as indicator kriging [6,7], disjunctive kriging [8e10], multigaussian kriging [11,12], probability kriging [13e15], lognormal kriging [16,17] and outlier restricted kriging [18,19] were developed These modifications resulted in more time consuming, computational complexity and overly expensive resource estimation processes.
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