Abstract
Although a major portion of the emitted energy from mine blast is sub-audible (lower frequency), there exist a component that is audible (high frequencies from 20 Hz to 20 KHz) and as such within the range of human hearing as noise. Unlike blast air overpressure (low frequency occurrence), noise prediction from mine blasting has received little scholarly attention in mining sciences. Noise from mine blast is considered a major detrimental blasting effect and can be a menace to nearby residents and workers in the mine. In this paper, a blast-induced noise level prediction model based on Brain Inspired Emotional Neural Network (BENN) is presented. The objective of this paper was to investigate the implementation possibility of the proposed BENN approach along with six other artificial intelligent methods, such as Backpropagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Generalised Regression Neural Network (GRNN), Group Method of Data Handling (GMDH), Least Squares Support Vector Machine (LSSVM) and Support Vector Machine (SVM). The study also implemented the standard Multiple Linear Regression (MLR) for comparison purposes. The statistical analysis carried out revealed that the BENN performed better than the other investigated methods. Thus, the BENN achieved very promising testing results of 1.619 dB, 3.076%, 0.0925%, 0.911 and 82.956% for root mean squared error (RMSE), mean absolute percentage error (MAPE), normalised root mean squared error (NRMSE), correlation coefficient (R) and variance accounted for (VAF). The implemented BENN can be useful in managing noise from mine blasting using site specific data.
Highlights
L oading, hauling, crushing, grinding and milling are the major constituents of downstream activities in mine operations which are geared towards achieving high mineral recovery rate
In view of Brain Inspired Emotional Neural Network (BENN) strengths, this study explored its capability as a new forecasting technique in blast-induced noise level prediction
It is important to note that BENN proposed in Lotfi [28] uses the genetic algorithm to fine-tune the numerical weights of the network
Summary
L oading, hauling, crushing, grinding and milling are the major constituents of downstream activities in mine operations which are geared towards achieving high mineral recovery rate. For optimum downstream operations, controlled blasting is the predominant measure usually employed to fragment consolidated mineral deposits in a surface or underground mine. Implementing controlled blasting increases mine efficiency and productivity which can lead to substantial savings in mining cost and control adverse blast-induced environmental effects such as noise, air blast, ground vibration, fly rock and over-break. Modelling and predicting adverse blast-induced environmental effects is a hot topic in mining sciences. The most notable blast-induced environmental effects that are well investigated include ground vibration [1,2], air blast [3], flyrock [4,5] and over-break [6,7]. The existing literature have only considered noise generated from the operation of different set of mine machineries used in surface and underground mines [8e16]
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