Developing a mathematical assessment model for blasting patterns management: Sungun copper mine

Blasting is one of the most important operations in the mining projects that has effective role in the whole operation physically and economically. Unsuitable blasting pattern may lead to unwanted events such as poor fragmentation, back break and fly rock. Multi attribute decision making (MADM) can be useful method for selecting the most appropriate blasting pattern among previously performed patterns. In this work, initially, from various already performed patterns, efficient and inefficient patterns are determined using data envelopment analysis (DEA). In the second step, after weighting impressive attributes using experts’ opinion, elimination Et choice translating reality (ELECTRE) was used for ranking the efficient patterns and recognizing the most appropriate pattern in the Sungun Copper Mine, Iran. According to the obtained results, blasting pattern with the hole diameter of 15.24 cm, burden of 3 m, spacing of 4 m and stemming of 3.2 m has selected as the best pattern and has selected for future operation.

[1]  Masoud Monjezi,et al.  Developing a new fuzzy model to predict burden from rock geomechanical properties , 2011, Expert Syst. Appl..

[2]  G. L. Mowrey,et al.  Blasting injuries in surface mining with emphasis on flyrock and blast area security. , 2004, Journal of Safety Research.

[3]  A. Arar,et al.  A “simple” geomatics-based approach for assessing water erosion hazard at montane areas , 2012, Arabian Journal of Geosciences.

[4]  T. Hudaverdi,et al.  Application of multivariate analysis for prediction of blast-induced ground vibrations , 2012 .

[5]  Lv Shuran,et al.  Applying BP Neural Network Model to Forecast Peak Velocity of Blasting Ground Vibration , 2011 .

[6]  Mohammad Ataei,et al.  Using TOPSIS approaches for predictive porphyry Cu potential mapping: A case study in Ahar-Arasbaran area (NW, Iran) , 2012, Comput. Geosci..

[7]  Mohammad Ataei,et al.  Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation , 2012, Arabian Journal of Geosciences.

[8]  Masoud Monjezi,et al.  Evaluation of flyrock phenomenon due to blasting operation by support vector machine , 2012, Neural Computing and Applications.

[9]  Masoud Monjezi,et al.  Optimization of Open pit Blast Parameters using Genetic Algorithm , 2011 .

[10]  A. Jones 10 – Rock Mechanics and Rock Engineering , 1989 .

[11]  Masoud Monjezi,et al.  Development of a fuzzy model to predict flyrock in surface mining , 2011 .

[12]  Masoud Monjezi,et al.  Prediction and controlling of flyrock in blasting operation using artificial neural network , 2011 .

[13]  Mario A. Morin,et al.  Monte Carlo simulation as a tool to predict blasting fragmentation based on the Kuz-Ram model , 2006, Comput. Geosci..

[14]  Thomas L. Saaty,et al.  Models, Methods, Concepts & Applications of the Analytic Hierarchy Process , 2012 .

[15]  M. Monjezi,et al.  Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach , 2012, Arabian Journal of Geosciences.

[16]  Mohammad Ataei,et al.  Development of an empirical model for predicting the effects of controllable blasting parameters on flyrock distance in surface mines , 2012 .

[17]  Saša Stojadinović,et al.  Prediction of flyrock trajectories for forensic applications using ballistic flight equations , 2011 .

[18]  H. Bakhshandeh Amnieh,et al.  Design of blasting pattern in proportion to the peak particle velocity (PPV): Artificial neural networks approach , 2012 .

[19]  Sang-ho Cho,et al.  Rock Fragmentation Control in Blasting , 2004 .

[20]  M. Monjezi,et al.  Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method , 2013, Rock Mechanics and Rock Engineering.

[21]  Kaveh Ahangari,et al.  An empirical relation to calculate the proper burden in blast design of open pit mines based on modification of the Konya relation , 2012 .

[22]  Masoud Monjezi,et al.  Evaluation of Blasting Patterns Using Operational Research Models / Ocena Planów Prac Strzałowych W Oparciu O Metody Badań Operacyjnych , 2013 .

[23]  Geert Wets,et al.  Benchmarking road safety: lessons to learn from a data envelopment analysis. , 2009, Accident; analysis and prevention.

[24]  Chiang Kao,et al.  Stochastic data envelopment analysis in measuring the efficiency of Taiwan commercial banks , 2009, Eur. J. Oper. Res..

[25]  Jianping Li,et al.  Parameter selection of support vector machines and genetic algorithm based on change area search , 2011, Neural Computing and Applications.

[26]  Ramakrishnan Ramanathan,et al.  Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process , 2006, Comput. Oper. Res..

[27]  Hesam Dehghani,et al.  Development of a model to predict peak particle velocity in a blasting operation , 2011 .

[28]  W. Cooper,et al.  Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software , 1999 .

[29]  Li Jiang,et al.  Structural safety criteria for blasting vibration based on wavelet packet energy spectra , 2011 .

[30]  Ching-Lai Hwang,et al.  Multiple attribute decision making : an introduction , 1995 .

[31]  Lawrence M. Seiford,et al.  Data envelopment scenario analysis for setting targets to electricity generating plants , 1999, Eur. J. Oper. Res..

[32]  Shanling Li,et al.  A super-efficiency model for ranking efficient units in data envelopment analysis , 2007, Appl. Math. Comput..