تعیین قابلیت خردایش ناشی از انفجار بر اساس نسبت سرعت موج طولی و بعد فرکتال بلوک‌های انفجاری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده فنی و مهندسی، دانشگاه تربیت مدرس

2 دانشکده مهندسی معدن و متالورژی، دانشگاه یزد

3 پژوهشکده فناوری‌های معدنکاری، دانشگاه یزد

4 گروه مهندسی نفت و معدن، دانشگاه آزاد اسلامی، واحد تهران جنوب

10.29252/anm.2020.11479.1376

چکیده

انفجار یکی از مهم‌ترین عملیات در معادن و فعالیت‌های عمرانی است و قابلیت انفجار نیز از مهم‌ترین پارامترهای توده‌سنگ محسوب می‌شود. نتایج انفجار را می‌توان به پدیده‌های مطلوب و نامطلوب تقسیم‌بندی نمود. خردایش سنگ یکی از نتایج مطلوب انفجار است که بر فرآیندهای بعدی (بارگیری و حمل) و صنایع پایین دست تاثیرگذار است. در زمینه تعیین شاخص خردایش ناشی از انفجار تحقیقات گسترده‌ای صورت گرفته است. عوامل موثر بر انفجار را می‌توان به دو دسته عوامل قابل کنترل و غیر قابل کنترل تقسیم‌بندی نمود. شکل و ابعاد بلوک‌های برجا در جبهه‌کارهای انفجاری از جمله عوامل غیر قابل کنترل است که در پژوهش‌های انجام شده، مورد توجه قرار نگرفته است. در این مقاله از داده‌های انفجاری و ویژگی‌های توده‌سنگ معادن چغارت، چادرملو و سه‌چاهون استفاده شده است. بعد فرکتال جهت کمی‌سازی اندازه ذرات به روش شمارش جعبه مورد استفاده قرار گرفته است. جهت بررسی ارتباط بین شاخص خردایش (D80) به عنوان پارامتر وابسته و پارامترهای مستقل خرج ویژه، مقاومت فشاری تک محوری، نسبت سرعت موج طولی و نسبت شکل بلوک‌ها، از مدل‌های رگرسیون خطی، رگرسیون غیرخطی و شبکه عصبی استفاده شده است. نتایج حاصل از رگرسیون خطی نشان‌دهنده آن است که شاخص خردایش با نسبت شکل بلوک‎ها دارای ارتباط معنادار است. ضریب تعیین بین شاخص خردایش و پارامترهای مستقل در رگرسیون خطی، غیرخطی و شبکه عصبی به ترتیب برابر 52/0، 70/0 و 96/0 به دست آمده است؛ همچنین ضریب VAF مدل‌های رگرسیون خطی، غیرخطی و شبکه عصبی به ترتیب برابر 18/3، 33/70 و 28/95 است که نشان‌دهنده توانایی شبکه عصبی جهت پیش‌بینی خردایش ناشی از انفجار است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Determination of Rock Fragmentation Based on Longitude Wave Velocity and Fractal Dimension

نویسندگان [English]

  • Morteza Baghestani 1
  • Masoud Monjezi 1
  • Alireza Yarahmadi Bafghi 2 3
  • Peyman Afzal 4
1 Dept. of Mining, Tarbiat Modares University, Tehran, Iran
2 Dept. of Mining and Metallurgy, Yazd University, Yazd, Iran
3 Dept. of Mining and Metallurgy, Yazd University, Yazd, Iran
4 Dept. of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
چکیده [English]

Summary
In this paper, the blasting data and rock mass characteristics of Chogart, Chadormalu, and Sechahum mines were used to predict the size distribution of rock fragmentation (D80). Rock fragmentation is affected by various parameters such as rock mass properties, in-situ blocks shape, blasting geometry, etc. To quantify the shape of in-situ blocks, fractal geometry is a suitable method. To predict the rock fragmentation (D80) based on independent variables (rock mass characteristics, in-situ block shape, and blasting geometry); linear/nonlinear regression and neural networks were used. The results showed that the nonlinear regression and neural network were the ability to predict the size distribution of rock fragmentation. 
 
Introduction
Due to economic reasons, drilling and blasting methods have been used in mining, quarrying industries, and civil projects. The results of blasting can be categorized into two i.e. favorable results (rock fragmentation, heave and move material) and unfavorable results (air overpressure, back break, ground vibration, and fly rock). Rock fragmentation due to blasting is influenced by several factors that are classified into three namely; explosive parameters, rock mass characteristics, and blast geometry. In recent decades, several empirical models have been proposed to predict rock fragmentation due to blasting. Nowadays, based on computer science advances,    regression analysis and artificial intelligence (AI) have been employed for rock fragmentation prediction.
 
Methodology and Approaches
In this research, fractal geometry was used to describe the rock mass shape. The fractal dimension of in-situ blocks was determined by the box-counting method. On the other hand, the uniaxial compressive strength (UCS) and longitude wave velocity (laboratory and in-situ) were considered as rock mass characteristics. Also, the powder factor (PF) was representative of blasting geometry and explosive parameters. The linear/nonlinear regression and neural network were used to investigate the relationship between the rock fragmentation and independence variables (rock mass characteristics, blasting geometry, rock mass shape).
 
Results and Conclusions
In this research, an attempt was made to predict the size distribution of rock fragmentation (D80)at the Central iron ore mines (Chogart, Chadormalu, and Sechahun) by linear/nonlinear regression and neural network. Linear regression results revealed that the independent variables have a significant effect on the dependent variable (D80). The results were shown the neural network has the superiority to the prediction of rock fragmentation.

کلیدواژه‌ها [English]

  • Rock Fragmentation
  • Fractal Dimension
  • Wave Velocity
  • Regression
  • Neural Network

اصل مقاله

عملیات حفاری و انفجار از لحاظ فنی و اقتصادی، در استخراج معادن و کارهای عمرانی به ‌عنوان یک جزء اصلی از چرخه عملیاتی مطرح است[1-3]. نتایج انفجار را می‌توان به دو دسته نتایج مطلوب (خردایش سنگ، جابجایی توده خردشده) و نتایج نامطلوب (لرزش زمین، پرتاب سنگ، لرزش هوا و عقب‌زدگی) تقسیم‌بندی نمود[4-8]. خردایش ناشی از انفجار بر صنایع پایین دست معدنی (خردایش، پرعیارسازی) تاثیرگذار است[9،10]. عوامل موثر بر خردایش ناشی از انفجار را می‌توان به عوامل قابل کنترل (سیستم آتشکاری، طراحی الگوها، مواد منفجره مصرفی) و عوامل غیر قابل کنترل (پارامترهای توده‌سنگ) تقسیم‌بندی نمود[11-15] (شکل 1).

مراجع

[1]           Akbari, M., Lashkaripour, G., Bafghi, A. Y., & Ghafoori, M. (2015). Blastability evaluation for rock mass fragmentation in Iran central iron ore mines. International Journal of Mining Science and Technology, 25(1), 59-66.
[2]           Lyana, K. N., Hareyani, Z., Shah, A. K., & Hazizan, M. M. (2016). Effect of geological condition on degree of fragmentation in a Simpang Pulai marble quarry. Procedia Chemistry, 19, 694-701.
[3]           Sasaoka, T., Takahashi, Y., Sugeng, W., Hamanaka, A., Shimada, H., Matsui, K., & Kubota, S. (2015). Effects of rock mass conditions and blasting standard on fragmentation size at limestone quarries. Open journal of geology, 5(05), 331.
[4]           Mohamad, E. T., Armaghani, D. J., Hasanipanah, M., Murlidhar, B. R., & Alel, M. N. A. (2016). Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique. Environmental Earth Sciences, 75(2), 174.
[5]           AminShokravi, A., Eskandar, H., Derakhsh, A. M., Rad, H. N., & Ghanadi, A. (2018). The potential application of particle swarm optimization algorithm for forecasting the air-overpressure induced by mine blasting. Engineering with Computers, 34(2), 277-285.
[6]           Murmu, S., Maheshwari, P., & Verma, H. K. (2018). Empirical and probabilistic analysis of blast-induced ground vibrations. International Journal of Rock Mechanics and Mining Sciences, 103, 267-274.
[7]           Bakhtavar, E., Nourizadeh, H., & Sahebi, A. A. (2017). Toward predicting blast-induced flyrock: a hybrid dimensional analysis fuzzy inference system. International journal of environmental science and technology, 14(4), 717-728.
[8]           Faramarzi, F., Mansouri, H., & Farsangi, M. E. (2013). A rock engineering systems based model to predict rock fragmentation by blasting. International Journal of Rock Mechanics and Mining Sciences, 60, 82-94.
[9]           MacKenzie, A. S. (1966, May). Cost of explosives—do you evaluate it properly. In Mining Congress Journal (Vol. 52, No. 5, pp. 32-41).
[10]         Taherkhani, H., & Doostmohammadi, R. (2015). Transportation costs: A tool for evaluating the effect of rock mass mechanical parameters on blasting results in open pit mining. Journal of Mining Science, 51(4), 730-742.
[11]         Singh, D. P., & Sastry, V. R. (1986). Influence of structural discontinuity on rock fragmentation by blasting. In Proceedings of the 6th international symposium on intense dynamic loading and its effects. Beijing. doi (Vol. 10).
[12]         Lyana, K. N., Hareyani, Z., Shah, A. K., & Hazizan, M. M. (2016). Effect of geological condition on degree of fragmentation in a Simpang Pulai marble quarry. Procedia Chemistry, 19, 694-701.
[13]         Singh, P. K., Roy, M. P., Paswan, R. K., Sarim, M., Kumar, S., & Jha, R. R. (2016). Rock fragmentation control in opencast blasting. Journal of Rock Mechanics and Geotechnical Engineering, 8(2), 225-237.
[14]         Latham, J. P., & Lu, P. (1999). Development of an assessment system for the blastability of rock masses. International Journal of Rock Mechanics and Mining Sciences, 36(1), 41-55.
[15]         Kulula, M. I., Nashongo, M. N., & Akande, J. M. (2017). Influence of Blasting Parameters and Density of Rocks on Blast Performance at Tschudi Mine, Tsumeb, Namibia. Journal of Minerals and Materials Characterization and Engineering, 5(06), 339.
[16]         Kuznetsov, V. M. (1973). The mean diameter of the fragments formed by blasting rock. Soviet Mining, 9(2), 144-148.
[17]         Cunningham, C. (1983). The Kuz-Ram Model for production of fragmentation from blasting. In Proc. 1^< st> Symp. on Rock Fragmentation by Blasting, Lulea.
[18]         Hjelmberg, H. (1983, August). Some ideas on how to improve calculations of the fragment size distribution in bench blasting. In 1st International Symposium on Rock Fragmentation by Blasting. Lulea University Technology Lulea, Sweden (pp. 469-494).
[19]         Roy, P. P., & Dhar, B. B. (1996). Fragmentation analyzing scale—a new tool for breakage assessment. In Proceedings 5th international symposium on rock fragmentation by blasting-FRAGBLAST (Vol. 5).
[20]         Kanchibotla, S. S., Valery, W., & Morrell, S. (1999, November). Modelling fines in blast fragmentation and its impact on crushing and grinding. In Explo ‘99–A conference on rock breaking, The Australasian Institute of Mining and Metallurgy, Kalgoorlie, Australia (pp. 137-144).
[21]         Djordjevic, N. (1999). A two-component model of blast fragmentation. In AusIMM Proceedings (Vol. 2, pp. 9-13).
[22]         Gheibie, S., Aghababaei, H., Hoseinie, S. H., & Pourrahimian, Y. (2009). Modified Kuz—Ram fragmentation model and its use at the Sungun Copper Mine. International Journal of Rock Mechanics and Mining Sciences, 46(6), 967-973.
[23]         Aler, J., Du Mouza, J., & Arnould, M. (1996). Evaluation of blast fragmentation efficiency and its prediction by multivariate analysis procedures. In International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts (Vol. 7, No. 33, p. 315A).
[24]         Chakraborty, A. K., Raina, A. K., Ramulu, M., Choudhury, P. B., Haldar, A., Sahu, P., & Bandopadhyay, C. (2004). Parametric study to develop guidelines for blast fragmentation improvement in jointed and massive formations. Engineering geology, 73(1-2), 105-116.
[25]         Bahrami, A., Monjezi, M., Goshtasbi, K., & Ghazvinian, A. (2011). Prediction of rock fragmentation due to blasting using artificial neural network. Engineering with Computers, 27(2), 177-181.
[26]         Hudaverdi, T., Kuzu, C., & Fisne, A. (2012). Investigation of the blast fragmentation using the mean fragment size and fragmentation index. International Journal of Rock Mechanics and Mining Sciences, 56, 136-145.
[27]         Ebrahimi, E., Monjezi, M., Khalesi, M. R., & Armaghani, D. J. (2016). Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bulletin of Engineering Geology and the Environment, 75(1), 27-36.
[28]         Karami, A., & Afiuni-Zadeh, S. (2013). Sizing of rock fragmentation modeling due to bench blasting using adaptive neuro-fuzzy inference system (ANFIS). International Journal of Mining Science and Technology, 23(6), 809-813.
[29]         SHI, X. Z., Jian, Z. H. O. U., WU, B. B., HUANG, D., & Wei, W. E. I. (2012). Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction. Transactions of Nonferrous Metals Society of China, 22(2), 432-441.
[30]         Mehrdanesh, A., Monjezi, M., & Sayadi, A. R. (2018). Evaluation of effect of rock mass properties on fragmentation using robust techniques. Engineering with Computers, 34(2), 253-260.
[31]         Yarahmadi Bafghi, A. R. (2013). Blastability Classification in Central Iron Ore Mines and Choghart Blastability Zoning. Mining Engineering Research Center, Yazd University, (In Persian).‎
[32]         Mandelbrot, B. B. (1982). The fractal geometry of nature (Vol. 1). New York: WH freeman.
[33]         Cheng, Q., Agterberg, F. P., & Ballantyne, S. B. (1994). The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration, 51(2), 109-130.
[34]         Agterberg, F. P. (1995). Multifractal modeling of the sizes and grades of giant and supergiant deposits. International Geology Review, 37(1), 1-8.
[35]         Cheng, Q., Xu, Y., & Grunsky, E. (2000). Integrated spatial and spectrum method for geochemical anomaly separation. Natural Resources Research, 9(1), 43-52.
[36]         Li, C., Ma, T., & Shi, J. (2003). Application of a fractal method relating concentrations and distances for separation of geochemical anomalies from background. Journal of Geochemical Exploration, 77(2-3), 167-175.
[37]         Afzal, P., Alghalandis, Y. F., Khakzad, A., Moarefvand, P., & Omran, N. R. (2011). Delineation of mineralization zones in porphyry Cu deposits by fractal concentration–volume modeling. Journal of Geochemical Exploration, 108(3), 220-232.
[38]         Hassanpour, S., & Afzal, P. (2013). Application of concentration–number (C–N) multifractal modeling for geochemical anomaly separation in Haftcheshmeh porphyry system, NW Iran. Arabian Journal of Geosciences, 6(3), 957-970.
[39]         Daya, A-A., & Moradi, R. (2018). Comparative analysis between concentration-number (CN) and concentration-area (CA) fractal models for separating anomaly from background in Siahrood 100,000 sheet, NW Iran. Journal of Analytical and Numerical Methods in Mining Engineering, 8(16), 87-95 (In Persian).‎
[40]         Geranian, H., Tokhmechi, B., & Heydari, A. (2014). Plotting Grade-Tonnage Curves with Fractal Methods and Comparing Them by Geostatistical Methods; a Case Study: Koh-e-Zar Gold Deposit in Torbat-e-Heydaryeh. Journal of Analytical and Numerical Methods in Mining Engineering, 3(6), 11-22 (In Persian).‎
[41]         Seyedrahimi-Niaraq, M-M., Ghavami Riabi, R., Khalukakaei, R., Hezareh, M., & Hendi, R. (2012). Comparison results of gold mineralization geochemical data modeling from probability and concentration- area fractal plots in separation of subpopulations. Journal of Analytical and Numerical Methods in Mining Engineering, 1(1), 24-31 (In Persian).‎
[42]         Afzal, P., Ahmadi, K., & Rahbar, K. (2017). Application of fractal-wavelet analysis for separation of geochemical anomalies. Journal of African Earth Sciences, 128, 27-36.
[43]         Zhao, Y., Huang, J., & Wang, R. (1993, December). Fractal characteristics of mesofractures in compressed rock specimens. In International journal of rock mechanics and mining sciences & geomechanics abstracts (Vol. 30, No. 7, pp. 877-882). Pergamon.
[44]         Ehlen, J. (2000). Fractal analysis of joint patterns in granite. International Journal of Rock Mechanics and Mining Sciences, 37(6), 909-922.
[45]         Billi, A., & Storti, F. (2004). Fractal distribution of particle size in carbonate cataclastic rocks from the core of a regional strike-slip fault zone. Tectonophysics, 384(1-4), 115-128.
[46]         Hamdi, E. (2008). A fractal description of simulated 3D discontinuity networks. Rock mechanics and Rock engineering, 41(4), 587-599.
[47]         Yasrebi, A. B., Wetherelt, A., Foster, P. J., Afzal, P., Coggan, J., & Ahangaran, D. K. (2013). Application of RQD- Number and RQD-Volume to delineate rock mass characterization in Kahang Cu-Mo porphyry deposit. Archives of Mining Sciences, 58(4), 1023-1035.
[48]         Ficker, T. (2017). Fractal properties of joint roughness coefficients. International Journal of Rock Mechanics and Mining Sciences, (94), 27-31.
[49]         Crum, S. V. (1990, January). Fractal concepts applied to bench-blast fragmentation. In The 31th US Symposium on Rock Mechanics (USRMS). American Rock Mechanics Association.
[50]         Ghosh, A., Daemen, J. J. K., & Van Zyl, D. (1990, January). Fractal-based approach to determine the effect of discontinuities on blast fragmentation. In The 31th US Symposium on Rock Mechanics (USRMS). American Rock Mechanics Association.
[51]         Russell, D. A., Hanson, J. D., & Ott, E. (1980). Dimension of strange attractors. Physical Review Letters, 45(14), 1175.
[52]         Zuo, R., Cheng, Q., & Xia, Q. (2009). Application of fractal models to characterization of vertical distribution of geochemical element concentration. Journal of Geochemical Exploration, 102(1), 37-43.
[53]         Jaya, V., Raghukanth, S. T. G., & Sonika Mohan, S. (2014). Estimating fractal dimension of lineaments using box counting method for the Indian landmass. Geocarto International, 29(3), 314-331.
[54]         Boadu, F. K., & Long, L. T. (1994, April). The fractal character of fracture spacing and RQD. In International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts (Vol. 31, No. 2, pp. 127-134). Pergamon.
[55]         Mou, D., & Wang, Z. W. (2016). Comparison of box counting and correlation dimension methods in well logging data analysis associate with the texture of volcanic rocks.
[56]         Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences.
روش های تحلیلی و عددی در مهندسی معدن
دوره 10، شماره 24
آبان 1399
صفحه 105-117

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  • تاریخ دریافت: 10 بهمن 1397
  • تاریخ بازنگری: 11 مهر 1399
  • تاریخ پذیرش: 11 مهر 1399

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