Flyrock is one of the most important environmental issues in mine blasting, which can affect
equipment, people and could cause fatal accidents. Therefore, minimization of this environmental
issue of blasting must be considered as the ultimate objective of many rock
removal projects. This paper describes a new minimization procedure of flyrock using
intelligent approaches, i.e., artificial neural network (ANN) and particle swarm optimization
(PSO) algorithms. The most effective factors of flyrock were used as model inputs while the
output of the system was set as flyrock distance. In the initial stage, an ANN model was
constructed and proposed with high degree of accuracy. Then, two different strategies
according to ideal and engineering condition designs were considered and implemented
using PSO algorithm. The two main parameters of PSO algorithm for optimal design were
obtained as 50 for number of particle and 1000 for number of iteration. Flyrock values were
reduced in ideal condition to 34 m; while in engineering condition, this value was reduced to
109 m. In addition, an appropriate blasting pattern was proposed. It can be concluded that
using the proposed techniques and patterns, flyrock risks in the studied mine can be significantly
minimized and controlled.
کلید واژگان :
environmental issue of blasting, flyrock, ANN, PSO, blasting pattern.
