A comparative study on the application of Regression-PSO and ANN methods to predict backbreak in open pit mines

• One of the most challenging safety problems in open pit mines is backbreak during blasting operation, and its prediction is very important for a technically and economically successful mining operation. To avoid backbreak, different parameters such as physicomechanical properties of rock mass, explosives properties and geometrical features of the blasting pattern should be considered. This paper presents a new solution of multiple linear regression (MLR), particle swarm optimization algorithm (PSO) and artificial neural networks (ANNs) to estimate the backbreak induced by bench blasting, based on major controllable blasting parameters. To this aim, Angouran mine in Iran was considered and blasting pattern parameters for ۷۳ operations were collected. In addition, back-break was measured in each operation. Considering the previous investigations and also collected data from the mine, burden, spacing, hole length, stemming, charge per delay, RQD, number of row and powder factor were selected as input parameters. In order to find the better solutions, the constructed models were implemented in PSO algorithms. Also, the prediction of backbreak was investigated using ANNs. According to the obtained results, the PSO algorithm is a suitable tool for optimizing models and obtaining more accurate prediction of backbreak. Among the presented empirical models, the optimized exponential model with PSO algorithm with a RMSE (۰.۳۱) and R۲ (۰.۸۷) shows the better results in prediction of backbreak and it is suitable for practical use in Angouran mine. Considering the sensitivity analysis, among the input parameters, length of stemming and charge per delay have shown the most and the least effect on the backbreak, respectively. The results of ANNs show that multilayer networks are more powerful and efficient than single-layer in prediction of backbreak.

کلیدواژه ها:

Backbreak ، Multiple regression analysis ، Artificial neural networks ، Particle swarm optimization algorithm ، Angouran mine