Mine-to-crusher policy: Planning of mine blasting patterns for environmentally friendly and optimum fragmentation using Monte Carlo simulation-based multi-objective grey wolf optimization approach

Abstract

The quality of rock fragmentation intensively affects downstream operations and operational costs. Besides, Environmental side effects are inevitable due to mine blasting despite improvements in blasting consequences such as fly-rock and back-break. This study concentrates on optimizing mine blasting patterns for environmentally friendly mineral production and minimizing operational costs by achieving environmental-oriented and economic objectives-based on a new framework using artificial intelligence techniques. A gene expression programming (GEP) based on Monte Carlo simulations (MCs) denoted that rock size distribution can be modeled and predicted without any uncertainty. Four main objectives involving operational costs, back-break, fly-rock, and toe volume were highlighted for minimizing in the optimization framework. The multi-objective model was implemented by applying it to a running mine and solved using the grey wolf optimization algorithm. As optimizing, 17 optimal blasting plans were achieved to implement in the different rock types. The multi-objective model was able to reduce mine to crusher cost as well as undesirable blasting consequences considerable favourite of mining managers.