تعداد نشریات | 161 |
تعداد شمارهها | 6,532 |
تعداد مقالات | 70,501 |
تعداد مشاهده مقاله | 124,102,612 |
تعداد دریافت فایل اصل مقاله | 97,209,009 |
Blasted muckpile modeling in open pit mines using an artificial neural network designed by genetic algorithm | ||
International Journal of Mining and Geo-Engineering | ||
مقاله 10، دوره 58، شماره 2، شهریور 2024، صفحه 211-220 اصل مقاله (968.39 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/ijmge.2024.367398.595116 | ||
نویسندگان | ||
S. M. Mahdi Mirabedi1؛ Mehdi Rahmanpour* 1؛ Yousef Azimi2؛ Hassan Bakhshandeh Amnieh1 | ||
1School of Mining, College of Engineering University of Tehran, Tehran, Iran. | ||
2Research Centre for Environment and Sustainable Development, RCESD, Department of Environment, Tehran, Iran. | ||
چکیده | ||
The shape of a blasted rock mass, or simply muckpile, affects the efficiency of loading machines. Muckpile is defined with two main parameters known as throw and drop, while several blasting parameters will influence the muckpile shape. This paper studies the prediction of muckpile shape in open-pit mines by applying an artificial neural network designed by a genetic algorithm. In that regard, a genetic algorithm has been used in preparing the neural network architecture and parameters. Moreover, input variables have been reduced using the principal component analysis. Finally, the best models for predicting throw and drop are determined. Analyzing the performance of the proposed models indicates their superiority in predicting muckpile shape. As a result, the Mean Squared Error of throw is 0.53 for train data and 1.24 for test data. While for the drop, the errors are 0.45 and 0.58 for the training and testing data. Furthermore, sensitivity analysis shows that specific-charge effects drop and throw more. | ||
کلیدواژهها | ||
Hybrid genetic algorithm neural network؛ Blasting, Muckpile؛ Principal component analysis | ||
مراجع | ||
[1] Kose H, Aksoy CO, Gonen A, Kun M, Malli T (2005) Economic evaluation of optimum bench height in quarries. Journal of the South African Institute of Mining and Metallurgy, 105(2):127-136
[2] Afeni TB 2009 Optimization of drilling and blasting operations in an open pit mine - the SOMAIR experience. Mining Science and Technology, 19(6):736-739
[3] Azimi Y, Osanloo M, Aakbarpour-Shirazi M, Bazzazi AA (2010) Prediction of the blastability designation of rock masses using fuzzy sets. Int. J. Rock Mech. Min. Sci., 47:1126-1140, DOI: 10.1016/j.ijrmms.2010.06.016
[4] Kahriman A, Ozer U, Karadogan A, Ozdemir K, Kaya E (2008) Effects of particle size distribution on loading performance. 34th Annual Conference on Explosives and Blasting Technique, USA, January, 279-284
[5] Doktan M (2001) Impact of blast fragmentation on truck-shovel fleet performance. 17th International Mining Congress and Exhibition of Turkey, Ankara, Turkey, June, 375-379.
[6] Michaud P, Lizotte Y, Scoble M (1997) Rock fragmentation and mining productivity: characterization and case studies, 17thAnnual Conference on Explosives and Blasting Technique, USA, February, 61-72
[7] Singh PK, Roy MP, Paswan RK, Sarim M, Kumar S, Jha RR (2016) Rock fragmentation control in opencast blasting, Journal of Rock Mechanics and Geotechnical Engineering, 8(2016), 225-237
[8] Mirabedi SMM, Khodaiari A, Jafari A, Yavari M (2017) The effect of important fragmented rock properties on the penetration rate of loader bucket, Geotech Geol Eng., DOI: 10.1007/s10706-017-0393-7
[9] Tosun A (2018) A modified Wipfrag program for determining muckpile fragmentation, The journal of the Southern African Institute of Mining and Metallurgy, 118(October), 1113-1119, DOI: 10.17159/2411-9717/2018/v118n10a13
[10] Leng, Z., Fan, Y., Gao, Q., & Hu, Y. (2020). Evaluation and optimization of blasting approaches to reducing oversize boulders and toes in open-pit mine. International Journal of Mining Science and Technology, 30(3), 373-380.
[11] Tosun, A. (2022). A new method for determining muckpile fragmentation formed by blasting. Journal of the Southern African Institute of Mining and Metallurgy, 122(11), 665-672.
[12] Segarra, P., Sanchidrian, J. A., López, L. M., & Querol, E. (2010). On the prediction of mucking rates in metal ore blasting. Journal of Mining Science, 46, 167-176.
[13] Sarma S, Kanchibotla W (2010) Mine to mill process integration and optimization – benefits and challenges, 36th Annual Conference on Explosives and Blasting Technique, USA, January, 349-369
[14] Singh SP, Narendrula R (2006) Factors affecting the productivity of loaders in surface mines. International Journal of Surface Mining, Reclamation and Environment, 20(01), 20-32, DOI: 10.1016/j.jrmge.2015.10.005
[15] Aler J, Du Mouza J, Arnould M (1996) Measurement of the fragmentation efficiency of rock mass blasting and its mining applications, International Journal of Rock Mechanics and Mining Sciences & Geomechanics, 33(01), 125-139
[16] Mishra AK, Sinha M, Rout M (2013) Cast blasting for improved mine economics, in Ghose AK, Joshi A, (Eds), Blasting in mines – new trends, Taylor & Francis Group, London, ISBN 978-0-415-62139-7, 73-80
[17] Hanspal S, Scoble M, Lizotte Y (1995) Anatomy of a blast muckpile: Influence on loading machine performance. 21th Annual Conference on Explosives and Blasting Techniques, Nashville, TN, USA, February, 371p
[18] Jimeno CL, Jimeno EL, Carcedo FJA (1995) Drilling and blasting of rocks, Ramiro, Y.V.D. (translate by De Ramiro, Y.V.), A.A. Balkema, Rotterdam, Brookfield, ISBN: 90-5410-199-7, 391p
[19] Adhikari GR (2000) Empirical methods for the calculation of the specific charge for surface blast design, Fragblast, Int. J. for Blasting and Fragmentation, 4:1, 19-33, DOI: 10.1080/13855140009408061
[20] Jhanwar, J. C., & Jethwa, J. L. (2000). The use of air decks in production blasting in an open pit coal mine. Geotechnical & Geological Engineering, 18, 269-287
[21] Silva, J., Li, L., & Gernand, J. M. (2018). Reliability analysis for mine blast performance based on delay type and firing time. International Journal of Mining Science and Technology, 28(2), 195-204.
[22] Zou Z. & Jun Y. (2020). Modelling blast movement and muckpile formation with the position-based dynamics method, International Journal of Mining, Reclamation and Environment, DOI: 10.1080/17480930.2020.1835210
[23] Yang RL, Kavetsky A, Mckenzie CK (1989) A two-dimensional kinematic model for predicting muckpile shape in bench blasting, International Journal of Mining and Geological Engineering, 7, 209-226
[24] Yang RL, Kavetsky A (1990) A three-dimensional model of muckpile formation and grade boundary movement open pit blasting, International Journal of Mining and Geological Engineering, 8, 13-34
[25] Morin MA, Ficarazzo F (2006) Monte-Carlo simulation as a tool to predict blasting fragmentation based on the Kuz–Ram model, Computers & Geosciences, 32, 352–359
[26] Singh PK, Roy MP, Roy A, Jha SK, Singh AKB (2010) Maximizing the throw while controlling vibration within safe limits in cast blasting, Rock Fragmentation by Blasting, Sanchidrian, J.A., (ed.), Taylor & Francis Group, London
[27] Mencacci S, Jacquet D, Vandenabelle O, Chavez R, Couvrat JF, Sarrey Y (2010) Six-Sigma methodology applied to blasting, Rock Fragmentation by Blasting, Sanchidrian, J.A., (ed.), Taylor & Francis Group, London
[28] Muller B, Hausmann J, Niedzwiedz H (2010) Control of rock fragmentation and muckpile geometry during production blasts (environmentally friendly blasting technique), Rock Fragmentation by Blasting, Sanchidrian JA, (ed.), Taylor & Francis Group, London
[29] Rosa DL, Thornton D (2011) Blast movement modelling and measurement, 35th APCOM symposium, Wollongong, NSW, 24 - 30 September
[30] Choudhary BS (2013) Firing patterns and its effect on muckpile Shape parameters and fragmentation in quarry blasts, International Journal of Research in Engineering and Technology, 2(9), 32-45
[31] Choudhary BS, Rai P (2013) Stemming plug and its effect on fragmentation and muckpile shape parameters, Int. J. Mining and Mineral Engineering, 4(4), 296-311
[32] Cardu M, Seccatore J, Vaudagna A, Rezende A, Galvao F, Bettencourt J, Tomi G, (2015) Evidences of the influence of the detonation sequence in rock fragmentation by blasting, Part II, REM: R. Esc. Minas, Ouro Preto, 68(4), 455-462, DOI: 10.1590/0370-44672014680219
[33] Singh T, Singh V (2005) An intelligent approach to prediction and control ground vibration in mines, Geotech Geol Eng, 23, 249-262
[34] Sharma, A., Mishra, A. K., & Choudhary, B. S. (2019). Impact of blast design parameters on blasted muckpile profile in building stone quarries. In Annales de Chimie Science des Materiaux, 43(1), 29-36
[35] Choudhary, B. S., & Arora, R. (2018). Influence of front row burden on fragmentation, muckpile shape, excavator cycle time, and back break in surface limestone mines. Iranian Journal of Earth Sciences, 10(1), 1-10.
[36] Choudhary, B. S. (2019). Effect of blast induced rock fragmentation and muckpile angle on excavator performance in surface mines. Mining of Mineral Deposits, 13(3), 119-126
[37] Rai P, Chatterjee S, Bandopadhyay S (2009) Neural network based selection of design parameters governing shape and powder factor of blasted muck piles: a case study, Mining Technology, 118(2), 67-78
[38] Raina AK Murthy VMSR (2016) Importance and sensitivity of variables defining throw and flyrock in surface blasting by artificial neural network method, Current Science, 111(9), 1524-1531
[39] Murthy VMSR, Kumar A, Sinha PK (2016) Prediction of throw in bench blasting using neural networks: an approach, Neural Computing and Applications, 29(1), 143-156, DOI: 10.1007/s00521-016-2423-4
[40] Vasylchuk YV, Deutsch CV (2017) Improved grade control in open pit mines, Mining Technology, DOI: 10.1080/14749009.2017.1363991
[41] Singh SP, VanDoorselaere D (2015) The relationship between blasting parameters and muckpile configuration, in Proceedings 11th International Symposium on Rock Fragmentation by Blasting, 369–374 (AusIMM)
[42] Singh SP, Cheung D (2017) Factors governing the muckpile Characteristics, International Society of Explosives Engineers, 43rd annual conference on explosives and blasting techniques, At Orlando, Florida, USA
[43] Gandomi AH, Alavi AH, Mousavi M, Tabatabaei SM (2011) A hybrid computational approach to derive new ground-motion prediction equations, Engineering Applications of Artificial Intelligence, 24(4), 717-732, DOI: 10.1016/j.engappai.2011.01.005
[44] Amiri M, Amnieh HB, Hasanipanah M, Khanli LM (2016) A new combination of artificial neural network and K-nearest neighbors’ models to predict blast-induced ground vibration and air-overpressure, Eng Comput, 32, 631-644.
[45] Azimi Y, Khoshrou SH, Osanloo M (2019) Prediction of blast induced ground vibration (BIGV) of quarry mining using hybrid genetic algorithm optimized artificial neural network, Measurement, 147, 106874, DOI: 10.1016/j.measurement.2019.106874.
[46] Gao W, Alqahtani AS, Mubarakali A, Mavaluru D, Khalafi S (2020) Developing an innovative soft computing scheme for prediction of air overpressure resulting from mine blasting using GMDH optimized by GA. Engineering with Computers 36, 647–654, DOI: 10.1007/s00366-019-00720-5
[47] Zhang S, Bui XN, Trung NT, Nguyen H, Bui H (2020) Prediction of rock size distribution in mine bench blasting using a novel ant colony optimization-based boosted regression tree technique, Nat Resour Res, 29, 867–886, DOI: 10.1007/s11053-019-09603-4
[48] Ke B, Nguyen H, Bui XN, Costache R (2021) Estimation of ground vibration intensity induced by mine blasting using a state-of-the-art hybrid autoencoder neural network and support vector regression model, Nat Resour Res, DOI: 10.1007/s11053-021-09890-w
[49] Castillo PA, Merelo JJ, González J, Rivas V, Romero G (1999) SA-Prop: Optimization of multilayer perceptron parameters using simulated annealing, International Work-Conference on Artificial Neural Networks, Springer, 661-670.
[50] Socha K, Blum C (2007) An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training, Neural Computing and Applications, 16, 235-247.
[51] Moghaddam MA, Golmezergi R, Kolahan F (2016) Multi-variable measurements and optimization of GMAW parameters for API-X42 steel alloy using a hybrid BPNN–PSO approach, Measurement, 92, 279-287.
[52] Campos LML, Oliveira RCL, Roisenberg M (2016) Optimization of neural networks through grammatical evolution and a genetic algorithm, Expert Syst. Appl., 56, 368-384.
[53] Ekonomou L (2010) Greek long-term energy consumption prediction using artificial neural networks, Energy, 35, 512-517.
[54] Azimi Y (2019) Prediction of seismic wave intensity generated by bench blasting using intelligence committee machines. International Journal of Engineering, 32(4), 617-627.
[55] Goldberg D (1989) Genetic Algorithms in Search, Optimization, and Machine Learning, Addison Wesley, Reading, Massachusetts
[56] Carvalho AR, Ramos FM, Chaves AA (2011) Metaheuristics for the feedforward artificial neural network (ANN) architecture optimization problem, Neural Computing and Applications, 20, 1273-1284.
[57] Deb K (2000) An efficient constraint handling method for genetic algorithms. Computer methods in applied mechanics and engineering, 186(2-4), 311-338.
[58] Deep K, Singh KP, Kansal ML, Mohan C (2009) A real coded genetic algorithm for solving integer and mixed integer optimization problems. Applied Mathematics and Computation, 212(2), 505-518.
[59] Cunningham P, Carney J, Jacob S (2000) Stability problems with artificial neural networks and the ensemble solution, Artif Intell Med, 20(3), 217–25, DOI: 10.1016/S0933-3657(00)00065-8
[60] Chan ZSH, Ngan HW, Rad AB, David AK, Kasabov N (2006) Short-term ANN load forecasting from limited data using generalization learning strategies, Neurocomputing, 70, 409–419, DOI: 10.1016/j.neucom.2005.12.131
[61] Shao Y, Lunetta RS (2012) Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points, ISPRS Journal of Photogrammetry and Remote Sensing 70, 78–87, DOI: 10.1016/j.isprsjprs.2012.04.001
[62] Li D, Chen H, Shi Q (2018) Learning from small datasets containing nominal attributes, Neurocomputing, 291, 226–236, DOI: 10.1016/j.neucom.2018.02.069
[63] Koziarski M, Krawczyk B, Wozniak M (2019) Radial-based oversampling for noisy imbalanced data classification, Neurocomputing, 343, 19–33, DOI: 10.1016/j.neucom.2018.04.089
[64] Espezua S, Villanueva E, Maciel CD, Carvalho A (2015) A Projection Pursuit framework for supervised dimension reduction of high dimensional small sample datasets, Neurocomputing, 149, 767–776, DOI: 10.1016/j.neucom.2014.07.057
[65] Yang X, Huang K, Zhang R, Goulermas JY, Hussain A (2018) A new two-layer mixture of factor analyzers with joint factor loading model for the classification of small dataset problems, Neurocomputing, 312, 352–363, DOI: 10.1016/j.neucom.2018.05.085
[66] Ojha VK, Abraham A, Snasel V (2017) Metaheuristic design of feedforward neural networks: A review of two decades of research, Engineering Applications of Artificial Intelligence, 60, 97-116, DOI: 10.1016/j.engappai.2017.01.013
[67] Jackson JE (1991) A user's guide to principal components. John Wiley & Sons, New York, USA, 592 p
[68] Gevrey M, Dimopoulos I, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models, Ecological modelling 160(3), 249-264, DOI: 10.1016/S0304-3800(02)00257-0
[69] Olden JD, Joy MK, Death RG (2004) An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data." Ecological modelling 178(3-4), 389-397, DOI: 10.1016/j.ecolmodel.2004.03.013 | ||
آمار تعداد مشاهده مقاله: 216 تعداد دریافت فایل اصل مقاله: 159 |