Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects
- Authors: Monjezi, Masoud , Mohamadi, Hasan , Barati, Bahare , Khandelwal, Manoj
- Date: 2014
- Type: Text , Journal article
- Relation: Arabian Journal of Geosciences Vol. 7, no. 2 (2014), p. 505-511
- Full Text: false
- Reviewed:
- Description: In the blasting operation, risk of facing with undesirable environmental phenomena such as ground vibration, air blast, and flyrock is very high. Blasting pattern should properly be designed to achieve better fragmentation to guarantee the successfulness of the process. A good fragmentation means that the explosive energy has been applied in a right direction. However, many studies indicate that only 20-30 % of the available energy is actually utilized for rock fragmentation. Involvement of various effective parameters has made the problem complicated, advocating application of new approaches such as artificial intelligence-based techniques. In this paper, artificial neural network (ANN) method is used to predict rock fragmentation in the blasting operation of the Sungun copper mine, Iran. The predictive model is developed using eight and three input and output parameters, respectively. Trying various types of the networks, it was found that a trained model with back-propagation algorithm having architecture 8-15-8-3 is the optimum network. Also, performance comparison of the ANN modeling with that of the statistical method was confirmed robustness of the neural networks to predict rock fragmentation in the blasting operation. Finally, sensitivity analysis showed that the most influential parameters on fragmentation are powder factor, burden, and bench height. © 2012 Saudi Society for Geosciences.
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
- Authors: Hosseini, Shahab , Mousavi, Amin , Monjezi, Masoud , Khandelwal, Manoj
- Date: 2022
- Type: Text , Journal article
- Relation: Resources Policy Vol. 79, no. (2022), p.
- Full Text: false
- Reviewed:
- Description: 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. © 2022 Elsevier Ltd