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

Shahab Hosseini, Amin Mousavi, Masoud Monjezi and Manoj Khandelwal

Resources Policy, 2022, vol. 79, issue C

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.

Keywords: Fragmentation; Blasting; Simulation; Optimization; Mining cost (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030142072200530X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:79:y:2022:i:c:s030142072200530x

DOI: 10.1016/j.resourpol.2022.103087

Access Statistics for this article

Resources Policy is currently edited by R. G. Eggert

More articles in Resources Policy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().