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Prediction of backbreak in the blasting operations using artificial neural network (ANN) model and statistical models (case study: Gol-e-Gohar iron ore mine No. 1)

Sirjani Abbas Khajouei, Sereshki Ferhang, Ataei Mohammad, Hosseini Mohammad Amir

Archives of Mining Sciences

2022

Vol. 67, no. 1

107--121

Backbreak is an undesirable phenomenon in blasting operations, which can bedefined as the undesirable destruction of rock behind the last row of explosive holes. To prevent and reduce its adverse effects, it is necessary to accurately predict backbreak in the blasting process. For this purpose, the data obtained from 66 blasting operations in Gol-e-Gohar iron ore mine No. 1 considering blast pattern design Parameters and geologic were collected. The Pearson correlation results showed that the parameters of the hole height, burden, spacing, specific powder, number of holes, and the uniaxial compressive strength had a significant effect on the backbreak. In this study, a multilayer perceptron artificial neural network with the 6-12-1 architecture and six multiple linear and nonlinear statistical models were used to predict the backbreakin the blasting operations. The results of this study demonstrated that the prediction rate of backbreak using the artificial neural network model with R2 = 0.798 and the rates of MAD, MSE, RMSE and, MAPE were0.79, 0.93, 0.97 and, 11.63, respectively, showed fewer minor error compared to statistical models. Based on the sensitivity analysis results, the most important parameters affecting the backbreak, including the hole height, distance between the holes in the same row, the row spacing of the holes, had the most significant effect on the backbreak, and the uniaxial compressive strength showed the lowest impact on it.

Mathematical relationship between ultimate pit limits generated by discounted and undiscounted block value maximization in open pit mining

Saleki Meisam, Kakaie Reza, Ataei Mohammad

Journal of Sustainable Mining

2019

Vol. 18, iss. 2

94--99

Ultimate pit limit is an important aspect of open pit mining. The optimal ultimate outline determines the tonnage of extractable ore, the volume of waste to be removed, the location of the subsidiary facilities, the location of ore and waste stockpiles, the life time of the mine and the estimated net present value (NPV) of the entire mining operation. Traditionally, there are two major approaches to optimizing the ultimate pit limit. One seeks to determine the ultimate pit using undiscounted profit maximization and the other by determining the optimal mining sequence of all blocks and discounting the value of the blocks. The outline with the highest cumulative NPV will be chosen as the final pit limit. For each of these approaches, different algorithms are presented. The aim of this paper is to present an analytical investigation about the mathematical relationship between sets of blocks of ultimate pits generated by each of these approaches in an ore body. This investigation is in fact the mathematical proof of the theorem that a discounted ultimate pit is smaller than or equal to the undiscounted pit. The results show that the discounted pit is always a subset of the undiscounted pit.