2020
11
3
0
17
1

Blast Design for Improved Productivity using a Modified Available Energy Method
http://jme.shahroodut.ac.ir/article_1778.html
10.22044/jme.2020.9506.1861
1
In this work, a new drilling and blasting design methodology is introduced and applied at a case study mine to improve productivity. For the case study copper mine, a blast diameter of 203 mm is proposed to be used in the ore zone to meet the new required production rate of 90mtpa from 75mtpa. Currently, the Konya and Walter’s model is used to generate drilling and blasting design at a blasthole diameter of 172 mm. The new drilling and blast design approach is advantageous in the sense that it generates a lower specific drilling value and predicts an average fragment size compared with the current method being used. In this regard, a modified available energy blast design method that incorporates the blastability index of ore zone in the calculation of the input powder factor is introduced. The results of the blast design simulations at a 203 mm blasthole diameter shows that the modified available energy model generates a drilling and blasting design with a specific drilling value that is 15.3% less than that generated by the Ash’s and Konya and Walter’s models. Further, the modified available energy model generates a blast design with a predicted average fragment size that is 3.4% smaller than that generated by the Ash’s model, and 6.7% smaller than that generated by the Konya and Walter’s model.
0

643
659


S.
Mulenga
Department of Mining Engineering, School of Mines, University of Zambia, Lusaka, Zambia
Zambia
sunday.mulenga@unza.zm


R.
Kaunda
Department of Mining Engineering, Colorado School of Mines, Colorado, USA
United States of America
rkaunda@mine.edu
Blasting optimization
Modified Available Energy
Specific drilling
Average fragment size
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Proceedings of the 7th Annual Conference of Explosives and Blasting Research, Las Vegas, 179–191.##[35]. Kou, S. and Rustan, P. (1993). Computerized design and result prediction of bench blasting. Proceedings of 4th International Symposium on Rock Fragmentation by Blasting, 263271.##[36]. Lownds, C.M. (1995). Prediction of fragmentation based on distribution of explosives energy. Proceedings of the 11th Annual Conference of Explosives and Blasting Research, Orlando, Florida, USA, 286–296.##[37]. Aler, J., Du Mouza, J. and Arnould. M. (1996). Evaluation of blast fragmentation efficiency and its prediction by multivariate analysis procedures. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts 33: 189–96.##[38]. Morin, M.A. and Ficarazzo, F. (2006). Monte carlo simulation as a tool to predict blasting fragmentation based on the KuzRam model, Computers and Geotechnics, 32, 352–369.##[39]. Ouchterlony, F. (2005). The Swebrec© function: linking fragmentation by blasting and crushing, Mininig Technology, 114, 29–44.##[40]. Gheibie S., Aghababaei, H., Hoseinie, S.H. and Pourrahimian, Y. (2009). Modified KuzRam fragmentation model and its use at the Sungun Copper Mine, International Journal of Rock Mechanics and Mining Sciences, 46, 967–973.##[41]. Gheibie S. and Aghababaei, H. (2010). Kuznetsov model’s efficiency in estimation of mean fragment size at the Sungun copper mine. Proceeding of 9th International Symposium on Rock Fragmentation by Blasting, 265–270.##[42]. Monjezi, M., Rezaei, M. and Yazdian Varjani, A. (2009). Prediction of Rock Fragmentation due to Blasting in GolEGohar Iron Mine Using Fuzzy Logic.” International Journal of Rock Mechanics and Mining Sciences 46(8): 1273–1280 1273–1280. doi:10.1016/j.ijrmms.2009.05.005.##[43]. Kulatilake, P.H.S.W., Qiong, W., Hudaverdi, T. and Kuzu, C. (2010). Mean particle size prediction in rock blast fragmentation using neural networks. 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1

A Preliminary Assessment of Rock Slope Stability in Tropical Climates: A Case Study at Lafarge Quarry, Perak, Malaysia
http://jme.shahroodut.ac.ir/article_1850.html
10.22044/jme.2020.9814.1901
1
The stability analysis of rock slopes is a complex task for the geotechnical engineers due to the complex nature of the rock mass in a tropical climate that often has discontinuities in several forms, and consequently, in several types of slope failures. In this work, a rock mass classification scheme is followed in a tropical environment using the Rock Mass Rating (RMR) and Geological Strength Index (GSI) combined with the kinematic investigation using the Rocscience Software Dips 6.0. The Lafarge quarry is divided into ten windows. In the RMR system, the five parameters uniaxial compressive strength (UCS), rock quality designation (RQD), discontinuity spacing, discontinuity condition, and groundwater conditions are investigated. The RMR values range from 51 to 70 (fair to good rock mass), and the GSI values range from 62 to 65 (good to fair rock mass). There is a good and positive correlation between RMR and GSI. The kinematic analysis reveals that window A is prone to critical toppling, window H to critical wedgeplanar failure, and window G to critical wedge failure. From the results obtained, it can be concluded that the kinematic analysis combined with the rock mass classification system provides a better understanding to analyze the rock slope stability in a tropical climate compared with considering the rock mass classification system individually.
0

661
673


K.S.
Shah
Strategic Mineral Niche, School of Materials and Mineral Resources Engineering, Universiti Sains Malaysia, Penang, Malaysia
Pakistan
kausarsultanshah@gmail.com


M. H.
Mohd Hashim
Strategic Mineral Niche, School of Materials and Mineral Resources Engineering, Universiti Sains Malaysia, Penang, Malaysia
Malaysia
mohd_hazizan@usm.my


K.S.
Ariffin
Strategic Mineral Niche, School of Materials and Mineral Resources Engineering, Universiti Sains Malaysia, Penang, Malaysia
Malaysia
kamarsha@usm.my


N. F.
Nordin
Strategic Mineral Niche, School of Materials and Mineral Resources Engineering, Universiti Sains Malaysia, Penang, Malaysia
Malaysia
faridzamil@student.usm.my
Slope Stability
Rock mass rating
Rock mass classification
Geological strength index
Kinematic analysis
[[1]. Basahel, H. and H. Mitri. (2017). Application of rock mass classification systems to rock slope stability assessment: a case study. Journal of rock mechanics and geotechnical engineering. 9 (6): p. 9931009.##[2]. Abdullah, R.A. (2018). Slope Stability Analysis Of Quarry Face at Karang Sambung District, Central Java, Indonesia. International Journal Of Civil Engineering & Technology (IJCIET). 9 (1): p. 857864.##[3]. Hoek, E. (2000). Practical rock engineering.##[4]. Huang, Y.H. (Year). Slope stability analysis by the limit equilibrium method: Fundamentals and methods. of Conference.: American Society of Civil Engineers.##[5]. Liu, S., L. Shao, and H. Li. (2015). Slope stability analysis using the limit equilibrium method and two finite element methods. Computers and Geotechnics. 63: p. 291298.##[6]. Pasternack, S. and S. Gao. (1988),. Numerical methods in the stability analysis of slopes. Computers & Structures. 30 (3): p. 573579.##[7]. Sharma, M. (2019). Analysis of Slope Stability of road cut Slopes of Srinagar, Uttrakhand, India. International Journal of Applied Engineering Research. 14 (3): p. 609615.##[8]. Umrao, R. (2011). Stability analysis of cut slopes using continuous slope mass rating and kinematic analysis in Rudraprayag district, Uttarakhand. Geomaterials. 1 (03): p. 79.##[9]. Pan, P.Z. (2019). Modeling of an excavationinduced rock fracturing process from continuity to discontinuity. Engineering Analysis with Boundary Elements. 106: p. 286299.##[10]. Duran, A. and K. Douglas. (Year). Experience with empirical rock slope design. in ISRM International Symposium. of Conference.: International Society for Rock Mechanics and Rock Engineering.##[11]. Hoek, E. (2007). Rock mass properties. Practical rock engineering: p. 190236.##[12]. Hack, R., D. Price, and N. Rengers. (2003). A new approach to rock slope stability–a probability classification (SSPC). Bulletin of Engineering Geology and the Environment. 62 (2): p. 167184.##[13]. Bieniawski, Z. (1993). Classification of rock masses for engineering: the RMR system and future trends, in Rock Testing and Site Characterization. Elsevier. p. 553573.##[14]. Pantelidis, L. (2009). Rock slope stability assessment through rock mass classification systems. International Journal of Rock Mechanics and Mining Sciences. 46 (2): p. 315325.##[15]. Wyllie, D.C. and C. Mah. (2014). Rock slope engineering. CRC Press.##[16]. Mohamed, A.I. and A.F. Bayram. (2020). Utilizing a geomechanical classification to preliminary analysis of rock slope stability along roadway d34041.42, southwest of Turkey: A case study. Turkish Journal of Engineering. 4 (1): p. 9.##[17]. Ansari, T.A., K.M. Sharma. and T. Singh. (2019). Empirical Slope Stability Assessment Along the Road Corridor NH7, in the Lesser Himalayan. Geotechnical and Geological Engineering. 37 (6): p. 53915407.##[18]. Sujatha, E.R. and V. Thirukumaran. (2018). Rock slope stability assessment using geomechanical classification and its application for specific slopes along KodaikkanalPalani Hill Road, Western Ghats, India. Journal of the Geological Society of India. 91 (4): p. 489495.##[19]. Bieniawski, Z. (1973). Engineering classification of jointed rock masses. Civil Engineer in South Africa. 15(12).##[20]. Hoek, E. and E.T. Brown. (1980). Empirical strength criterion for rock masses. Journal of Geotechnical and Geoenvironmental Engineering. 106 (ASCE 15715).##[21]. Marinos, P. and E. Hoek. (Year), GSI: a geologically friendly tool for rock mass strength estimation. in ISRM international symposium. of Conference.: International Society for Rock Mechanics and Rock Engineering.##[22]. Hoek, E. and E.T. Brown. (1997). Practical estimates of rock mass strength. International journal of rock mechanics and mining sciences. 34 (8): p. 11651186.##[23]. Hoek, E., P. Marinos. and M. Benissi. (1998). Applicability of the Geological Strength Index (GSI) classification for very weak and sheared rock masses. The case of the Athens Schist Formation. Bulletin of Engineering Geology and the Environment. 57 (2): p. 151160.##[24]. Hoek, E. (1999). Putting numbers to geology—an engineer's viewpoint. Quarterly Journal of Engineering Geology and Hydrogeology. 32 (1): p. 119.##]
1

Investigation of Effect of Number of Lifters on Performance of PilotScale SAG Mills Using Discrete Element Method
http://jme.shahroodut.ac.ir/article_1688.html
10.22044/jme.2020.9045.1793
1
The number of lifters of mill shell liners, mill rotation speed, and filling percentage of grinding media are three of the most important parameters influencing the charge behavior and the trajectory of ball motion inside the SAG mills, and consequently, their performance. In this paper, the milling operation of pilotscale SAG mills using the discrete element method (DEM) is investigated. First, a pilotscale SAG mill with dimensions of 3.0 m × 1.5 m with no lifter is simulated. Then by adding, respectively, one, two, four, eight, sixteen, and thirtytwo rectangle lifter(s), six other independent simulations are performed. The effects of the number of lifters on the two new parameters introduced by the authors, i.e. ‘head height’ and ‘impact zone length’ as well as on creation of cascading, cataracting, and centrifuging motions for balls at two different mill speeds, i.e. 70% and 80% of its critical speed (NC), are evaluated. Also in order to validate the simulation results, a laboratoryscale SAG mill is simulated. The results obtained indicate that the optimum number of lifters for pilotscale SAG mills is between 16 and 32 lifters with medium thickness. Liners with the number of lifters in this range require less mill speed to create cataract motions. However, liners with the number of lifters less than this range require a higher mill speed. Also liners with the number of lifters beyond this range require less mill speed, and can cause centrifugal motions in the balls. Comparison of the simulations related to the laboratoryscale SAG mill with experimental results demonstrates a good agreement, which validates the DEM simulations and the software used.
0

675
693


S.
Kolahi
Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
Iran
sajad.kolahi@shahroodut.ac.ir


M.
Jahani Chegeni
Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
Iran
m.jahani1983@gmail.com
DEM simulation
Pilot Scale SAG Mill
Number of Lifters
Head Height
Impact zone length
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(2006). Investigation of performance of programming approaches and languages used for numerical simulation of granular material by the discrete element method. Computer Physics Communications. 175 (6): 404415.##[38]. Delaney, G.W., Cleary, P.W., Morrison, R.D., Cummins, S. and Loveday, B. (2013). Predicting breakage and the evolution of rock size and shape distributions in Ag and SAG mills using DEM. Minerals Engineering. 50: 132139.##[39]. Ting, J.M., Khwaja, M., Meachum, L.R. and Rowell, J.D. (1993). An ellipse‐based discrete element model for granular materials. International Journal for Numerical and Analytical Methods in Geomechanics. 17 (9): 603623.##[40]. Shmulevich, I. (2010). State of the art modeling of soil–tillage interaction using discrete element method. Soil and Tillage Research. 111 (1): 4153.##[41]. Cleary, P.W. and Sawley, M.L. (2002). DEM modelling of industrial granular flows: 3D case studies and the effect of particle shape on hopper discharge. Applied Mathematical Modelling. 26 (2): 89111.##[42]. Munjiza, A. and Cleary, P.W. (2009). Industrial particle flow modelling using discrete element method. Engineering Computations.##[43]. Cleary, P.W. and Sinnott, M.D. (2008). Assessing mixing characteristics of particlemixing and granulation devices. Particuology. 6 (6): 419444.##[44]. Cleary, P.W. (2010). DEM prediction of industrial and geophysical particle flows. Particuology. 8 (2): 106118.##[45]. Cleary, P.W. and Morrison, R.D. (2009). Particle methods for modelling in mineral processing. International Journal of Computational Fluid Dynamics. 23 (2): 137146.##[46]. Just, S., Toschkoff, G., Funke, A., Djuric, D., Scharrer, G., Khinast, J. and Kleinebudde, P. (2013). Experimental analysis of tablet properties for discrete element modeling of an active coating process. AAPS PharmSciTech. 14 (1): 402411.##[47]. McBride, W. and Cleary, P.W. (2009). An investigation and optimization of the ‘OLDS’elevator using Discrete Element Modeling. Powder Technology. 193 (3): 216234.##[48]. Goniva, C., Kloss, C., Deen, N.G., Kuipers, J.A. and Pirker, S. (2012). Influence of rolling friction on single spout fluidized bed simulation. Particuology. 10 (5): 582591.##[49]. Goniva, C., Kloss, C., Hager, A. and Pirker, S. (2010, June). An open source CFDDEM perspective. In Proceedings of OpenFOAM Workshop, Göteborg (pp. 2224).##[50]. Kloss, C., Goniva, C., Aichinger, G. and Pirker, S. (2009). Comprehensive DEMDPMCFD simulationsmodel synthesis, experimental validation and scalability. In Proceedings of the seventh international conference on CFD in the minerals and process industries, CSIRO, Melbourne, Australia.##[51]. Weerasekara, N.S., Powell, M.S., Cleary, P.W., Tavares, L.M., Evertsson, M., Morrison, R.D. and Carvalho, R.M. (2013). The contribution of DEM to the science of comminution. Powder Technology, 248, 324.##]
1

Application of Sequential Gaussian Conditional Simulation to Underground Mine Design Under Grade Uncertainty
http://jme.shahroodut.ac.ir/article_1540.html
10.22044/jme.2019.7333.1582
1
In mining projects, all uncertainties associated with a project must be considered to determine the feasibility study. Grade uncertainty is one of the major components of technical uncertainty that affects the variability of the project. Geostatistical simulation, as a reliable approach, is the most widely used method to quantify risk analysis to overcome the drawbacks of the estimation methods used for an entire ore body. In this work, all the algorithms developed by numerous researchers for optimization of the underground stope layout are reviewed. After that, a computer program called stope layout optimizer 3D is developed based on a previously proposed heuristic algorithm in order to incorporate the influence of grade variability in the final stope layout. Utilizing the sequential gaussian conditional simulation, 50 simulations and a kriging model are constructed for an underground copper vein deposit situated in the southwest of Iran, and the final stope layout is carried out separately. It can be observed that geostatistical simulation can effectively cope with the weakness of the kriging model. The final results obtained show that the frequency of economic value for all realizations varies between 6.7 M$ and 30.7 M$. This range of variation helps designers to make a better and lower risk decision under different conditions.
0

695
709


F.
Sotoudeh
Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
Iran
farzad.sotoudeh@gmail.com


M.
Ataei
Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
Iran
ataei@shahroodut.ac.ir


R.
Kakaie
Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
Iran
r_kakaie@shahroodut.ac.ir


Y.
Pourrahimian
School of Mining and Petroleum Engineering, University of Alberta, Alberta, Canada
Canada
yashar.pourrahimian@ualberta.ca
Underground Mining
grade uncertainty
Geostatistical Simulation
heuristic algorithm, SLO 3D
[[1]. Ross, J.G. (2004). Risk and uncertainty in portfolio characterization. Journal of Petroleum Science and Engineering. 44(1): 4153.##[2]. Dominy, S.C. Annels, A.E. and Noppe, M. (2002). Errors and uncertainty in Ore Reserve estimatesOperator beware. In Proceedings, Underground Operators Conference. (pp. 121126).##[3]. Morley, D. (1999). Financial impact of resource/reserve uncertainty. Journal of the Southern African Institute of Mining and Metallurgy. 99(6): 293301.##[4]. Dimitrakopoulos, R. (1990). Conditional simulation of intrinsic random functions of order k. Mathematical geology. 22(3): 361380.##[5]. Armstrong, M. and Dowd, P.A.(Eds.). (2013). Geostatistical Simulations: Proceedings of the Geostatistical Simulation Workshop, Fontainebleau, France. 27–28 May 1993 (Vol. 7). Springer Science & Business Media.##[6]. Smith, M. and Dimitrakopoulos, R. (1999). The influence of deposit uncertainty on mine production scheduling. International Journal of Surface Mining, Reclamation and Environment. 13(4): 173178.##[7]. Kumral, M. and Dowd, P.A. (2001). Shortterm scheduling for industrial minerals using multiobjective simulated annealing, APCOM 2001, Phoenix, Arizona.##[8]. Ramazan, S. and Dimitrakopoulos, R. (2007). Stochastic optimization of longterm production scheduling for open pit mines with a new integer programming formulation. Orebody Modelling and Strategic Mine Planning, Spectrum Series. 14: 359365.##[9]. Koushavand, B. AskariNasab, H. and Deutsch, C.V. (2014). A linear programming model for longterm mine planning in the presence of grade uncertainty and a stockpile. International Journal of Mining Science and Technology. 24(4): 451459.##[10]. Dimitrakopoulos, R. Farrelly, C.T. and Godoy, M. (2002). Moving forward from traditional optimization: grade uncertainty and risk effects in openpit design. Mining Technology. 111(1): 8288.##[11]. Dimitrakopoulos, R. Martinez, L. and Ramazan, S. (2007). A maximum upside/minimum downside approach to the traditional optimization of open pit mine design. Journal of Mining Science. 43(1): 7382.##[12]. Leite, A. and Dimitrakopoulos, R. (2007). Stochastic optimization model for open pit mine planning: application and risk analysis at copper deposit. Mining Technology. 116(3): 109118.##[13]. Montiel, L. Dimitrakopoulos, R. and Kawahata, K. (2016). Globally optimizing openpit and underground mining operations under geological uncertainty. Mining Technology. 125(1): 214.##[14]. Grieco, N. and Dimitrakopoulos, R. (2007). Managing grade risk in stope design optimization: probabilistic mathematical programming model and application in sublevel stoping. Mining technology. 11##6(2):4957.##[15]. Dimitrakopoulos, R. and Grieco, N. (2009). Stope design and geological uncertainty: quantification of risk in conventional designs and a probabilistic alternative. Journal of Mining Science. 45(2): 152163.##[16]. Riddle, J.M. (1977). A dynamic programming solution of a blockcaving mine layout. 14th International Symposium on Application of Computers and Operations Research in the Minerals Industries. Society for Mining, Metallurgy and Exploration (pp. 767780).##[17]. Deraisme, J. De Fouquet, C. and Fraisse, H. (1984). Geostatistical orebody model for computer optimization of profits from different underground mining methods. 18th APCOM IMM.##[18]. Cheimanoff, N.M. Deliac, E.P. and Mallet, J.L. (1989). GEOCAD: an alternative CAD and artificial intelligence tool that helps moving from geological resources to mineable reserves. In 21st International Symposium on the Application of Computers and Operations Research in the Mineral Industry. (pp. 471478).##[19]. GEOCAD, (2017).##[20]. AtaeePour, M. (2005). A critical survey of the existing stope layout optimization techniques. Journal of Mining Science. 41(5): 447466.##[21]. Alford, C, (1995), Optimization in underground mine design. Proceedings of the 25th International APCOM Symposium. The Australasian Institute of Mining and Metallurgy, Melbourne.##[22]. CAE Studio 3. (2014).##[23]. Ovanic, J. and Young, D.S. (1999). Economic optimization of open stope geometry. In 28th international APCOM symposium. Colorado school of Mines, Golden, Colorado. USA (pp. 855862).##[24]. LINGO and optimization modeling, (2017).##[25]. GAMS Development Corporation, (2017). Washington DC, USA.##[26]. ILOG CPLEX's mathematical optimization technology, (2017). IBM Corporation##[27]. Mirzaeian, Y. and Ataeepour, M. (2011). Optimization of Stope Geometry Using Piecewise Linear Function and MIP Approach. Journal of Civil & Environmental Engineering and Science Technology. 43(1): pp.79–87. (in Persian).##[28]. MPS, Ketron Optimization mathematical programming system, (2011).##[29]. Ataeepour, M. (2000). A Heuristic Algorithm to Optimize Stope Boundaries, Ph.D. Thesis, University of Wollongong, Australia.##[30]. AtaeePour, M. (2004). Optimization of stope limits using a heuristic approach. Mining Technology. 113(2): 123128.##[31]. Carwse, I. (2001). multiple pass floating stope process, In Proceedings of the Fourth Biennial Conference: Strategic Mine Planning. Melbourne: Australasian Institute of Mining and Metallurgy.##[32]. Jalali, S.E. and Ataeepour, M. (2004). A 2D Dynamic Programming Algorithm to Optimize Stope Boundaries, Proceedings of the 13th Symposium on Mine Planning and Equipment Selection, Rotterdam, Balkema. pp.45–52.##[33]. Jalali, S.E. Ataeepour, M. and Shahriar, K. (2007). Rigorous Algorithms to Optimize Stope Boundaries; Capabilities, Restrictions and Applications, Modern Management of Mine Producing, Geology and Environmental Protection. Albena, Bulgaria.##[34]. Jalali, S.E. Ataeepour, M. Shahriyar, K. and ElahiZeyni, E. (2007). A Computer Program to Optimize Stope Boundaries Using Probable Stope Algorithm. Iranian Journal of Mining Engineering (IRJME). 2(3): pp.7–14, (in Persian).##[35]. Jalali, S.E. Ataeepour, M. Shahriar, K. ElahiZeyni, E. and Nikbin, V. (2016). Computer Based Optimization of Underground Mining Area. Journal of Civil & Environmental Engineering and Science Technology. 48(4): pp.475–489. (in Persian).##[36]. Grieco, N. and Dimitrakopoulos, R. (2007). Managing grade risk in stope design optimization: probabilistic mathematical programming model and application in sublevel stoping. Mining technology. 116(2): 4957.##[37]. Dimitrakopoulos, R. and Grieco, N. (2009). Stope design and geological uncertainty: quantification of risk in conventional designs and a probabilistic alternative. Journal of Mining Science. 45(2): 152163.##[38]. Jalali, S.E. and Hosseini, H. (2009). Optimization of Extraction Range in Underground Mining Using a Greedy Algorithm. Journal of Science and Research in Mining Engineering. 4(9): pp.1–11, (in Persian).##[39]. Cormen, T.H. Leiserson, C.E. Rivest, R.L. and Stein, C. (2001). Greedy algorithms. Introduction to algorithms. 1: 329355.##[40]. Topal, E. and Sens, J. (2010). A new algorithm for stope boundary optimization. Journal of Coal Science and Engineering (China). 16(2): 113119.##[41]. Bai, X. Marcotte, D. and Simon, R. (2013). Underground stope optimization with network flow method. Computers & Geosciences. 52: 361371.##[42]. Sandanayake, D.S.S. Topal, E. and Asad, M.W.A. (2015). A heuristic approach to optimal design of an underground mine stope layout. Applied Soft Computing. 30: 595603.##[43]. Sandanayake, D.S.S., Topal, E. and Asad, M.W.A. (2015). Designing an optimal stope layout for underground mining based on a heuristic algorithm. International Journal of Mining Science and Technology. 25(5): 767772.##[44]. Nikbin, V., Ataeepour, M., Shahriar, K. and Pourrahimian, Y. (2018). A 3D approximate hybrid algorithm for stope boundary optimization. Computers & Operations Research.##[45]. Visual Studio, (2013).##[46]. Sotoudeh, F. Kakaie, R. and Ataei, M. (2017). 'Development of a computer program for underground mine stope optimisation using a heuristic algorithm', in M Hudyma & Y Potvin (eds). Proceedings of the First International Conference on Underground Mining Technology. Australian Centre for Geomechanics. Perth. pp. 689700.##[47]. Manchuk, J. and Deutsch, C. (2008). Optimizing stope designs and sequences in underground mines. SME Transactions. 324. 6775.##[48]. Ashgari, O. and Esfahani, N.M. (2013). A new approach for the geological risk evaluation of coal resources through a geostatistical simulation. Arabian Journal of Geosciences. 6(3): 929943.##[49]. Yunsel, T.Y. (2012). Risk quantification in grade variability of gold deposits using sequential Gaussian simulation. Journal of Central South University. 19(11): 32443255.##[50]. Dimitrakopoulos, R. (1998). Conditional simulation algorithms for modelling ore body uncertainty in open pit optimization. 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Stochastic environmental research and risk assessment. 28(7): 19131927.##[55]. Safikhani, M. Asghari, O. and Emery, X. (2016). Assessing the accuracy of Sequential Gaussian Simulation through statistical testing. Stochastic Environmental Research and Risk Assessment, 111.##[56]. Emery, X. (2004). Testing the correctness of the sequential algorithm for simulating Gaussian random fields. Stochastic Environmental Research and Risk Assessment. 18(6): 401413.##[57]. Webster, R. and Oliver, M.A. (2007). Geostatistics for environmental scientists. John Wiley and Sons.##[58]. SGEMS: The Stanford Geostatistical Earth Modeling Software, (2008).##[59]. Emery, X. and Peláez, M. (2011). Assessing the accuracy of sequential Gaussian simulation and cosimulation. Computational Geosciences. 15(4): 673689.##[60] Tatiya, R.R. (2005). Classification Stoping Methods. In Surface and Underground Excavations’, London: Taylor & Francis Group. 415–431.##[61] Villaescusa, E. (2000). A review of sublevel stoping. MassMin. 577–590##]
1

On Applicability of Some Indirect Tests for Estimation of Tensile Strength of Anisotropic Rocks
http://jme.shahroodut.ac.ir/article_1696.html
10.22044/jme.2020.9220.1813
1
The tensile strength of rocks plays a noteworthy role in their failure mechanism, and its determination can be beneficial in optimizing the design of the rock structures. Schistose rocks due to their inherent anisotropy in different foliation directions show a diverse strength at each direction. The purpose of this work was to compare and assess the tensile strength of phyllite, which was obtained in direct and indirect tensile tests in different foliation directions. To this end, several phyllite specimens with different foliation angles (0º, 30º, 45º, 60º, and 90º) related to the loading axis (β) were prepared. Finally, the direct tensile test, diametrical and axial point load tests, Brazilian test, and Schmidt hammer test were conducted on 188 samples. The results of the experimental tests revealed that the maximum and minimum tensile strengths in direct tensile testing tension were directly related to the angles of 0º and 90º. Also it was observed that the Brazilian tensile strength overestimated the tensile strength. Furthermore, an exponential correlation was introduced between the direct tensile strength and the Brazilian tensile strength.
0

711
720


F.
Rastegar
Rock Mechanics Division, School of Engineering, Tarbiat Modares University, Tehran, Iran
Iran
rastegar.uotm@gmail.com


H. R.
Nejati
Rock Mechanics Division, School of Engineering, Tarbiat Modares University, Tehran, Iran
Iran
h.nejati@modares.ac.ir


A.
Ghazvinian
Rock Mechanics Division, School of Engineering, Tarbiat Modares University, Tehran, Iran
Iran
hadi@modares.ac.ir


M. R.
Hadei
Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran
Iran
hadei@eng.ikiu.ac.ir


A.
Nazerigivi
Faculty of Civil Engineering, University of Minho, Braga, Portugal
portugal
am.nazeri@gmail.com
tensile strength
anisotropy
schistose
direction of foliation
[[1]. Hudson, J.A., Brown, E.T. and Rummel, F. (1972, March). The controlled failure of rock discs and rings loaded in diametral compression. In International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts (Vol. 9, No. 2, pp. 241248). Pergamon.##[2]. A. Coviello, R. Lagioia, and R. Nova, “On the Measurement of the Tensile Strength of Soft Rocks”, Rock Mechanics and Rock Engineering, Vol. 38, No. 4, 2005, pp 251–273.##[3]. Nova, R. and Zaninetti, A. (1990, August). An investigation into the tensile behaviour of a schistose rock. In International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts (Vol. 27, No. 4, pp. 231242). Pergamon.##[4]. Goodman, R.E. (1989). Introduction to rock mechanics (Vol. 2). New York: Wiley.##[5]. Liao, J.J., Yang, M.T. and Hsieh, H.Y. (1997). Direct tensile behavior of a transversely isotropic rock. International Journal of Rock Mechanics and Mining Sciences. 34 (5): 837849.##[6]. Nazerigivi, A., Nejati, H.R., Ghazvinian, A. and Najigivi, A. (2018). Effects of SiO2 nanoparticles dispersion on concrete fracture toughness. Construction and Building Materials, 171, 672679.##[7]. Ghazvinian, A., Nejati, H.R., Sarfarazi, V. and Hadei, M.R. (2013). Mixed mode crack propagation in low brittle rocklike materials. Arabian Journal of Geosciences. 6 (11): 44354444.##[8]. Gurocak, Z., Solanki, P., Alemdag, S. and Zaman, M.M. (2012). New considerations for empirical estimation of tensile strength of rocks. Engineering Geology, 145, 18.##[9]. Amadei, B. (1996, April). Importance of anisotropy when estimating and measuring in situ stresses in rock. In International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts (Vol. 33, No. 3, pp. 293325). Pergamon.##[10]. Cho, J. W., Kim, H., Jeon, S. and Min, K.B. (2012). Deformation and strength anisotropy of Asan gneiss, Boryeong shale, and Yeoncheon schist. International journal of rock mechanics and mining sciences (1997), 50, 158169.##[11]. Dai. F. and Xia, K. (2009). “Tensile strength anisotropy of Barre Granite”, ROCKENG09: Proceedings of the 3rd CANUS Rock Mechanics Symposium, Toronto, May 2009 (Edition: M. Diederichs, and G. Grasselli) 2009.##[12]. Barla, G. and Innaurato, N. (1973). Indirect tensile testing of anisotropic rocks. Rock mechanics, 5(4), 215230.##[13]. Nazerigivi, A., Nejati, H.R., Ghazvinian, A. and Najigivi, A. (2017). Influence of nanosilica on the failure mechanism of concrete specimens. Computers and Concrete, 19(4), 429434.##[14]. Nejati, H.R. and Ghazvinian, A. (2014). Brittleness effect on rock fatigue damage evolution. Rock mechanics and rock engineering, 47(5), 18391848.##[15]. Tien, Y.M., Kuo, M.C. and Juang, C.H. (2006). An experimental investigation of the failure mechanism of simulated transversely isotropic rocks. International journal of rock mechanics and mining sciences, 43(8), 11631181.##[16]. Hobbs, D.W. (1964, May). The tensile strength of rocks. In International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts (Vol. 1, No. 3, pp. 385396). Pergamon.##[17]. Gamaneh Kav Consulting Engineers, “Rock mechanics test results of water transmission system project of Azad dam to Ravansar”, Report No. 1, 2006.##[18]. Tavallali, A. and Vervoort, A. (2010). Effect of layer orientation on the failure of layered sandstone under Brazilian test conditions. International journal of rock mechanics and mining sciences. 47 (2): 313322.##[19]. Debecker, B. and Vervoort, A. (2009). Experimental observation of fracture patterns in layered slate. International journal of fracture. 159 (1): 5162.##[20]. Li, D. and Wong, L.N.Y. (2013). The Brazilian disc test for rock mechanics applications: review and new insights. Rock mechanics and rock engineering. 46 (2): 269287.##[21]. Fairhurst, C. (1964, October). On the validity of the ‘Brazilian’test for brittle materials. In International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts (Vol. 1, No. 4, pp. 535546). Pergamon.##[22]. Mellor, M. and Hawkes, I. (1971). Measurement of tensile strength by diametral compression of discs and annuli. Engineering Geology. 5 (3): 173225.##[23]. Scull, P., Franklin, J., Chadwick, O.A. and McArthur, D. (2003). Predictive soil mapping: a review. Progress in Physical Geography. 27 (2): 171197.##[24]. Bieniawski, Z.T. and Bernede, M.J. (1979, April). Suggested methods for determining the uniaxial compressive strength and deformability of rock materials: Part 1. Suggested method for determining deformability of rock materials in uniaxial compression. In International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts (Vol. 16, No. 2, pp. 138140). Pergamon.##[25]. Franklin, J.A. (1985, April). Suggested method for determining point load strength. In International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts (Vol. 22, No. 2, pp. 5160). Pergamon.##[26]. Chau, K.T. and Wong, R.H.C. (1996). Uniaxial compressive strength and point load strength of rocks. In International journal of rock mechanics and mining sciences & geomechanics abstracts (Vol. 33, No. 2, pp. 183188). Pergamon.##[27]. Russell, A.R. and Wood, D.M. (2009). Point load tests and strength measurements for brittle spheres. International Journal of Rock Mechanics and Mining Sciences. 46 (2): 272280.##[28]. Schmidt, E. (1951). A nondestructive concrete tester. Concrete, 59, 3435.##[29]. Miller, R.P. (1965). Engineering classification and index properties for intact rock. PhD Thesis, University of Illinois.##[30]. Barton, N. and Choubey, V. (1977). The shear strength of rock joints in theory and practice. Rock mechanics, 10(12), 154.##[31]. Brown, E.T. (1981). Rock characterization testing and monitoring (No. BOOK). Pergamon press.##[32]. Hucka, V. (1965). A rapid method of determining the strength of rocks in situ. In International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts (Vol. 2, No. 2, pp. 127134). Pergamon.##[33]. Poole, R.W. and Farmer, I.W. (1980). Consistency and repeatability of Schmidt hammer rebound data during field testing. International Journal of Rock Mechanics and Mining Science. 17 (3).##[34]. Fowell, R.J. and RJ, F. (1976). FACTORS INFLUENCING THE CUTTING PERFORMANCE OF A SELECTIVE TUNNELLING MACHINE.##[35]. Demirdag, S., Yavuz, H. and Altindag, R. (2009). The effect of sample size on Schmidt rebound hardness value of rocks. International Journal of Rock Mechanics and Mining Sciences. 46 (4): 725730.##[36]. Brown, E.T. (1981). Rock characterization testing and monitoring (No. BOOK). Pergamon press.##[38]. Chau, K.T. (1998). Analytic solutions for diametral point load strength tests. Journal of engineering mechanics. 124 (8): 875883.##[39]. Heidari, M., Khanlari, G.R., Kaveh, M.T. and Kargarian, S. (2012). Predicting the uniaxial compressive and tensile strengths of gypsum rock by point load testing. Rock mechanics and rock engineering, 45(2), 265273.##[40]. Tsidzi, K.E.N. (1990). The influence of foliation on point load strength anisotropy of foliated rocks. Engineering Geology. 29 (1): 4958.##[41]. Basu, A. and Aydin, A. (2004). A method for normalization of Schmidt hammer rebound values. International Journal of Rock Mechanics and Mining Sciences. 41 (7): 12111214.##]
1

A Comprehensive Study of Several MetaHeuristic Algorithms for OpenPit Mine Production Scheduling Problem Considering Grade Uncertainty
http://jme.shahroodut.ac.ir/article_1718.html
10.22044/jme.2020.9127.1803
1
It is significant to discover a global optimization in the problems dealing with large dimensional scales to increase the quality of decisionmaking in the mining operation. It has been broadly confirmed that the longterm production scheduling (LTPS) problem performs a main role in mining projects to develop the performance regarding the obtainability of constraints, while maximizing the whole profits of the project in a specific period. There is a requirement for improving the scheduling methodologies to get a good solution since the production scheduling problems are nondeterministic polynomialtime hard. The current paper introduces the hybrid models so as to solve the LTPS problem under the condition of grade uncertainty with the contribution of Lagrangian relaxation (LR), particle swarm optimization (PSO), firefly algorithm (FA), and bat algorithm (BA). In fact, the LTPS problem is solved under the condition of grade uncertainty. It is proposed to use the LR technique on the LTPS problem and develop its performance, speeding up the convergence. Furthermore, PSO, FA, and BA are projected to bring uptodate the Lagrangian multipliers. The consequences of the case study specifies that the LR method is more influential than the traditional linearization method to clarify the largescale problem and make an acceptable solution. The results obtained point out that a better presentation is gained by LR–FA in comparison with LRPSO, LRBA, LRGenetic Algorithm (GA), and traditional methods in terms of the summation net present value. Moreover, the CPU time by the LRFA method is approximately 16.2% upper than the other methods.
0

721
736


K.
Tolouei
Department of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
Iran
kamyar.tolouei@gmail.com


E.
Moosavi
Department of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
Iran
se.moosavi@yahoo.com


A.H.
Bangian Tabrizi
Department of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
Iran
ah.bangian@gmail.com


P.
Afzal
Department of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
Iran
peymanafzal@gmail.com


A.
Aghajani Bazzazi
Department of Mining Engineering, University of Kashan, Kashan, Iran
Iran
a.aghajani.bazzazi@gmail.com
OpenPit Mine
longterm production scheduling
grade uncertainty
Lagrangian relaxation
Metaheuristics Methods
[[1]. Lerchs, H. and Grossmann, I.F. (1965). Optimum design of openpit mines. Transactions CIM. LXVIII: 17–24.##[2]. Pana, M.T. (1965). The simulation approach to open pit design. Paper presented at the APCOM Symposium.##[3]. Korobov, S. (1974). Method for determining optimal open pit limits. Rapport Technique EP, 74.##[4]. David, M., Dowd, P.A. and Korobov, S. (1974). Forecasting departure from panning in open pit design and grade control. In 12th Symposium on the application of computers and operaton research in the mineral industries (APCOM) volume 2 (Golden, Colo: Colorado School of Mines). F13142.##[5]. Dowd, P., and Onur, A. (1992). Optimising open pit design and sequencing. Proceedings 23rd Application of Computer in Mineral Industry. 411422.##[6]. Dowd, P.A. and Onur, A.H. (1993). Openpit optimization part 1: optimal openpit design. Transaction of the Insitution of Mining and Metallurgy (Section A: Mining Industry). 102: A95–104.##[7]. Onur, A.H. and Dowd, P.A. (1993). 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1

Effect of Segmental Joint on Internal Forces in Tunnel Lining under Seismic Loading by Numerical Method
http://jme.shahroodut.ac.ir/article_1751.html
10.22044/jme.2020.8979.1785
1
Although segmental tunnel linings are often used for seismic areas, the influence of segment joints on the segmental lining behavior under seismic loading has not been thoroughly considered in the literature. This paper presents the results of a numerical study investigating the effects of the rotational, axial, and radial joint stiffness of the longitudinal joints on the structural forces in segmental tunnel lining under seismic loading. A 3D finite element method is adapted to establish elaborate numerical models of the segments. The validity of the numerical model was tested by comparing the results obtained with the wellknown analytical methods presented by Wang and Penzien. The results demonstrate that by increasing the rotational stiffness of the segmental joint, the bending moment increases. When the rotational stiffness ratio is less than 0.5, the positive and negative bending moment variations are more. The numerical modeling results show the variations in the bending moment and the difference between the positive and negative bending moment values increased by increasing the acceleration of seismic loading. Moreover, it is significant for the values. By increasing the rotational stiffness ratio of the segmental joint, the axial force ratio decreases. By increasing the axial and shear stiffness ratio of segmental joint, the variations in the bending moment and axial force in segmental lining is not significant and is ignorable in designing segmental lining.
0

737
751


Gh.H.
Ranjbar
Faculty of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Iran
engineerranjbar@yahoo.com


K.
Shahriar
Faculty of Mining and Metallurgy Engineering, Amirkabir University of Technology, Tehran, Iran
Iran
k.shahriar@aut.ac.ir


K.
Ahangari
Faculty of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Iran
kaveh.ahangari@gmail.com
Segmental Lining
joint stiffness
Seismic Loading
Numerical method
Internal Forces
[[1]. Bobet, A. (2003). Effect of pore water pressure on tunnel support during static and seismic loading. Tunnelling and Underground Space Technology. 18: 377–393##[2]. Gomes, R.C., Gouveia, F., Torcato, D. and Santos, J. (2015). Seismic response of shallow circular tunnels in twolayered ground. Soil Dynamics and Earthquake Engineering. 75: 37–43##[3]. Mohammad, C., Pakbaz, A. and Akbar, Yareevand. (2005). 2D analysis of circular tunnel against earthquake loading. Tunnelling and Underground Space Technology. 20: 411–417##[4]. Wang, J.N. (1993). Seismic design of tunnels: A state of the art approach. Parsons Brinkerhoff Quad & Douglas Inc. New York. Monograph 7.##[5]. Penzien, J. and Ching, L.W. (1998). Stresses in linings of bored tunnels. Journal of Earthquake Engineering and Structural Dynamics. 27: 283300.##[6]. Penzien, J. (2000). Seismically induced racking of tunnel linings. Journal of Earthquake Engineering and Structural Dynamics. 29: 683–691.##[7]. Hashash, Y.M.A., Hook, J., Schmidt, B. and Yao, J.I. (2001). Seismic design and analysis of underground structure. Journal of Tunnelling and Underground Space Technology. 16: 247–293.##[8]. Hashash, Y.M.A., Park, D. and Yao, J.I. (2005). Ovaling deformations of circular tunnels under seismic loading: an update on seismic design and analysis of underground structures. Journal of Tunnelling and Underground Space Technology. 20: 435441.##[9] Park, K., Tantayopin, K. and Tontavanich B. (2006). Analytical solutions for seismic design of tunnel lining in Bangkok MRT subway. Proceedings of the International Symposium on Underground Excavation and Tunneling. Bangkok. Thailand. 541550.##[10]. Sederat, H., Kozak, A., Hashash, Y.M.A., Shamsabadi. A. and Krimotat. A. (2009). Contact interface in seismic analysis of circular tunnels. Journal of Tunnelling and Underground Space Technology. 24: 482–90.##[11]. Do, N.A., Dias, D., Oreste, P. and DjeranMaigre, I. (2015). 2D numerical investigation of segmental tunnel lining under seismic loading. Soil Dynamics and Earthquake Engineering. 72: 66–76.##[12]. Kramer, G.J., Sederat, H., Kozak, A., Liu, A. and Chai, J. (2007). Seismic response of precast tunnel lining. Proceedings of the rapid exacavation and tunneling conference. 1225–1242.##[13] Sliteen, L., Mroueh, H. and Sadek, M. (2013). Threedimensional modeling of the behaviour of shallow tunnel under seismic load. 20th Congress Rock Mechanics, France.##[14]. Nikkhah, M., Mousavi, S.S., Zare, Sh. and Khademhosseini, O. (2017). Evaluation of structural analysis of tunnel segmental lining using beamspring method and forcemethod (Case study: Chamshir water conveyance tunnel). Journal of Mining & Environment. Vol.8, No.1, 111130.##[15]. Caratellia, A., Medaa, A., Rinaldia, Z., GiulianiLeonardib, S. and Renaultb, F. (2018). On the behavior of radial joints in segmental tunnel linings. Tunnelling and Underground Space Technology. 71: 180–192##[16]. DO, N.A. (2014). Numerical analysis of segmental tunnel lining under static and dynamic loads. Ph.D thesis. Civil Engineering. University of Lyon. France.##[17]. Wood, A.M.M. (1975). The circular tunnel in elastic ground. Geotechnique. 25: 115–127.##[18]. JSCE. (1996). Japanese standard for shield tunnelling. Tunnel Engineering Committee English Edition of Japanese Standard for Tunnelling, Subcommitteee Japan Scociety of Civil Engineers. The third edition. Tokyo.##[19]. Liu, J.H. and Hou, X.Y. (1991). Shielddriven tunnels. China Railway Press. Beijing. China. 152303.##[20]. Lee, K.M. and Ge, X.W. (2001). The equivalence of a jointed shielddriven tunnel lining to a continuous ring structure. Canadian Geotechnical Journal. 38: 461483.##[21]. Blom, C.B., Vander Horst, E.J. and Jovanovis, P.S. (1999). Threedimensional structural analyses of the shielddriven “Green Heart” tunnel of the highspeed line south.##[22]. Blom, C.B. (2002). Design philosophy of concrete linings for tunnel in soft soils. Ph.D. dissertation. Delft University. Netherlands.##[23]. Naggar, H.E. and Hinchberger, S.D. (2008). An analytical solution for jointed tunnel linings in elastic soil or rock. Canadian Geotechnical Journal. 45: 15721593.##[24] Kasper, T. and Meschke, G. (2004). A 3D finite element simulation model for TBM tunnelling in soft ground. International Journal for Numerical and Analytical Methods in Geomechanics. 28: 14411460.##[25]. Kasper, T. and Meschke, G. (2006). A numerical study of the effect of soil and grout material properties and cover depth in shield tunnelling. Computers and Geotechnics. 33(45): 234247.##[26]. Hefny, A. and Chua, H. (2006). An investigation into the behavior of jointed tunnel lining. Tunnelling and Underground Space Technology. 21: 428.##[27]. Munsterman, W.P. and Brugman, M.H.A. (2009). Determination of Final Geotechnical Parameters for Calculations. Mashhad Urban Railway Line 2. Rahab Engineering Establishment. Document No.: MUR2ARTRE01R0401.##[28]. ABAQUS software, analysis user’s guide, 2016.##]
1

Comparison of Copper Dissolution in Chalcopyrite Concentrate Bioleaching with Acidianus Brierleyi in Different Initial pH Values
http://jme.shahroodut.ac.ir/article_1756.html
10.22044/jme.2020.9447.1853
1
Although bioleaching of chalcopyrite by thermophilic microorganisms enhances the rate of copper recovery, a high temperature accelerates iron precipitation as jarosite, which can bring many operational problems in the industrial processes. In this research work, the bioleaching of chalcopyrite concentrate by the thermophilic Acidianus brierleyi was studied, and the microbial growth, copper dissolution, iron oxidation, and jarosite precipitation were monitored in different initial pH (pHi) values. Bacterial growth was greatly affected by pHi. While the bacterial growth was delayed for 11 days with a pHi value of 0.8, this delay was reduced to nearly one day for a pHi value of 1.2. Two stages of copper recovery were observed during all the tests. A high pHi value caused a fast bacterial growth in the first stage and severe jarosite precipitation in the later days causing a sharp decline in the bacterial population and copper leaching rate. The copper recoveries after 11 days were 25%, 78%, 84%, 70%, 56%, and 39% for the pHi values of 0.8, 1.0, 1.2, 1.3, 1.5, and 1.7, respectively. Sulfur and jarosite were the main residues of the bioleaching tests. It was revealed that the drastic effect of jarosite precipitation on the microbial growth and copper recovery was mainly caused by the ferric iron depletion from solution rather than passivation of the chalcopyrite surface. A slow precipitation of crystalline jarosite did not cause a passive chalcopyrite surface. The mechanisms of chalcopyrite bioleaching were discussed.
0

753
764


M. R.
Samadzadeh Yazdi
Mining and Metallurgical Engineering Department, Mining Technologies Research Center (MTRC), Yazd University, Yazd, Iran
Iran
samadzadehyazdi@yazd.ac.ir


M.
Abdollahi
Mineral Processing Division, Mining Engineering Department, Tarbiat Modares University, Tehran, Iran
Iran
minmabd@modares.ac.ir


S. M.
Mousavi
Biotechnology Division, Chemical Engineering Department, Tarbiat Modares University, Tehran, Iran
Iran
mousavi_m@modares.ac.ir


A.
Khodadadi Darban
Mineral Processing Division, Mining Engineering Department, Tarbiat Modares University, Tehran, Iran
Iran
akdarban@modares.ac.ir
Bioleaching
Chalcopyrite
Acidianus brierleyi
Initial pH
Iron oxidation
Jarosite precipitation
[[1]. Stott, M.B., Sutton, D.C., Watling, H.R. Franzmann, P.D. (2003). Comparative Leaching of Chalcopyrite by Selected Acidophilic Bacteria and Archaea. Geomicrobiol J. 20 (3): 215–230.##[2]. Vilcáez, J., Suto, K. and Inoue, C. (2008). Bioleaching of chalcopyrite with thermophiles. International Journal of Mineral Processing. 88 (12): 3744.##[3]. Manafi, Z., Abdollahi, H. and Tuovinen, O.H. (2013). Shake flask and column bioleaching of a pyritic porphyry copper sulphide ore. International Journal of Mineral Processing. 119: 1620.##[4]. Abdollahi, H., Shafaei, S.Z., Noaparast, M., Manafi, Z., Niemelä, S.I. and Tuovinen, O.H. (2014). Mesophilic and thermophilic bioleaching of copper from a chalcopyritecontaining molybdenite concentrate. International Journal of Mineral Processing. 128: 2532.##[5]. Lotfalian, M., Ranjbar, M., Fazaelipoor, M.H., Schaffie, M. and Manafi, Z. (2015). Continuous bioleaching of chalcopyritic concentrate at high pulp density. Geomicrobiology Journal. 32 (1): 4250.##[6]. Lotfalian, M., Schaffie, M., Darezereshki, E., Manafi, Z. and Ranjbar, M. (2012). Column bioleaching of lowgrade chalcopyritic ore using moderate thermophile bacteria. Geomicrobiology Journal. 29 (8): 697703.##[7]. Ahmadi, A., Schaffie, M., Manafi, Z. and Ranjbar, M. (2010). Electrochemical bioleaching of high grade chalcopyrite flotation concentrates in a stirred bioreactor. Hydrometallurgy. 104 (1): 99105.##[8]. Panda, S., Akcil, A., Pradhan, N. and Deveci, H. (2015). Current scenario of chalcopyrite bioleaching: A review on the recent advances to its heapleach technology. Bioresour Technol. 196: 694–706.##[9]. Konishi, Y., Asai, S., Tokushige, M. and Suzuki, T. (1999). Kinetics of the bioleaching of chalcopyrite concentrate by acidophilic thermophile Acidianus brierleyi. Biotechnology Progress. 15 (4): 681688.##[10]. Castro, C., Urbieta, M.S., Cazón, J.P. and Donati, E.R. (2019). Metal biorecovery and bioremediation: whether or not thermophilic are better than mesophilic microorganisms. Bioresource technology.##[11]. Norris, P.R., Burton, N.P. and Clark, D.A. (2013). Mineral sulfide concentrate leaching in high temperature bioreactors. Miner Eng. 48:10–19.##[12]. Zhu, W., Xia, J., Yang, Y., Nie, Z., Peng, A. and Liu, H. (2013). Thermophilic archaeal community succession and function change associated with the leaching rate in bioleaching of chalcopyrite. Bioresour Technol. 133: 405–413.##[13]. Brierley, C.L. and Brierley, J.A. (1973). A chemoautotrophic and thermophilic microorganism isolated from an acid hot spring. Canadian Journal of microbiology. 19 (2): 183188.##[14]. Zillig, W., Stetter, K.O., Wunderl, S., Schulz, W., Priess, H. and Scholz, I. (1980). The Sulfolobus“Caldariella” group: taxonomy on the basis of the structure of DNAdependent RNA polymerases. Archives of Microbiology. 125 (3): 259269.##[15]. Segerer, A., Neuner, A., Kristjansson, J.K. and Stetter, K.O. (1986). Acidianus infernus gen. nov., sp. nov., and Acidianus brierleyi comb. nov.: facultatively aerobic, extremely acidophilic thermophilic sulfurmetabolizing archaebacteria. International Journal of Systematic and Evolutionary Microbiology. 36 (4): 559564.##[16]. Vilcáez, J., Suto, K. and Inoue, C. (2008). Response of thermophiles to the simultaneous addition of sulfur and ferric ion to enhance the bioleaching of chalcopyrite. Minerals Engineering. 21 (15): 10631074.##[17]. Sand, W., Gehrke, T., Jozsa, P.G. and Schippers, A. (2001). (Bio) chemistry of bacterial leaching—direct vs. indirect bioleaching. Hydrometallurgy. 59 (23): 159175.##[18]. Liang, Y.T., Han, J.W., Ai, C.B. and Qin, W.Q. (2018). Adsorption and leaching behaviors of chalcopyrite by two extreme thermophilic archaea. Transactions of Nonferrous Metals Society of China. 28 (12): 25382544.##[19] Mahmoud, A., Cézac, P., Hoadley, A.F., Contamine, F. and d'Hugues, P. (2017). A review of sulfide minerals microbially assisted leaching in stirred tank reactors. International Biodeterioration & Biodegradation. 119: 118146.##[20]. Zhao, H., Zhang, Y., Zhang, X., Qian, L., Sun, M., Yang, Y. and Qiu, G. (2019). The dissolution and passivation mechanism of chalcopyrite in bioleaching: An overview. Minerals Engineering. 136: 140154.##[21]. Esmailbagi, M. R., Schaffie, M., Kamyabi, A. and Ranjbar, M. (2018). Microbial assisted galvanic leaching of chalcopyrite concentrate in continuously stirred bioreactors. Hydrometallurgy. 180: 139143.##[22]. Jafari, M., Abdollahi, H., Shafaei, S. Z., Gharabaghi, M., Jafari, H., Akcil, A. and Panda, S. (2019). Acidophilic bioleaching: a review on the process and effect of organic–inorganic reagents and materials on its efficiency. Mineral Processing and Extractive Metallurgy Review. 40 (2): 87107.##[23]. Karamanev, D.G., Nikolov, L.N. and Mamatarkova, V. (2002). Rapid simultaneous quantitative determination of ferric and ferrous ions in drainage waters and similar solutions. Miner Eng. 15 (5): 341–346.##[24]. Johnson, D.B., Kanao, T. and Hedrich, S. (2012). Redox Transformations of Iron at Extremely Low pH: Fundamental and Applied Aspects. Front Microbiol. 3: 96.##[25]. Bonnefoy, V., Holmes, D.S. (2012). Genomic insights into microbial iron oxidation and iron uptake strategies in extremely acidic environments. Environ Microbiol. 14 (7): 1597–1611.##[26]. Bishop, J.L. and Murad, E. (2005). The visible and infrared spectral properties of jarosite and alunite. Am Mineral. 90 (7): 11001107.##[27]. Klauber, C. (2008). A critical review of the surface chemistry of acidic ferric sulphate dissolution of chalcopyrite with regards to hindered dissolution. Int J Miner Process. 86: 1–17##[28]. Vargas, T., DavisBelmar, C.S. and Cárcamo, C. (2014). Biological and chemical control in copper bioleaching processes: When inoculation would be of any benefit? Hydrometallurgy. 150: 290–298.##[29]. Zhu, W., Xia, J., Yang, Y., Nie, Z., Zheng, L. and Ma, C. (2011). Sulfur oxidation activities of pure and mixed thermophiles and sulfur speciation in bioleaching of chalcopyrite. Bioresour Technol. 102 (4): 3877–3882.##[30]. ValdebenitoRolack, E., RuizTagle, N., Abarzúa, L., Aroca, G. and Urrutia, H. (2017). Characterization of a hyperthermophilic sulphuroxidizing biofilm produced by archaea isolated from a hot spring. Electron J Biotechnol. 25: 58–63.##]
1

A New Mathematical Model for Production Scheduling in Sublevel Caving Mining Method
http://jme.shahroodut.ac.ir/article_1773.html
10.22044/jme.2020.9139.1804
1
Production scheduling in underground mines is still a manual process, and achieving a truly optimal result through manual scheduling is impossible due to the complexity of the scheduling problems. Among the underground mining methods, sublevel caving is a common mining method with a high production rate for hard rock mining. There are limited studies about longterm production scheduling in the sublevel caving method. In this work, for sublevel caving production scheduling optimization, a new mathematical model with the objective of net present value (NPV) maximization is developed. The general technical and operational constraints of the sublevel caving method such as opening and developments, production capacity, sublevel mining geometry, and ore access are considered in this model. Prior to the application of the scheduling model, the block model is processed to remove the unnecessary blocks. For this purpose, the floating stope algorithm is applied in order to determine the ultimate mine boundary and reduce the number of blocks that consequently reduces the running time of the model. The model is applied to a bauxite mine block model and the maximum NPV is determined, and then the mine development network is designed based on the optimal schedule.
0

765
778


M.
Shenavar
Faculty of Mining and Metallurgy Engineering, Amirkabir University of Technology, Tehran, Iran
Iran
m_shenavar@aut.ac.ir


M.
Ataeepour
Faculty of Mining and Metallurgy Engineering, Amirkabir University of Technology, Tehran, Iran
Iran
map60@aut.ac.ir


M.
Rahmanpour
School of mining, college of engineering, University of Tehran, Iran
Iran
mrahmanpour@ut.ac.ir
Mathematical Modeling
linear programming
Underground Mining
Sublevel Caving
Production Scheduling
Optimization
Net Present Value
[[1]. Pourrahimian, Y., AskariNasab, H., Dwayne D. and Tannant. (2013). A multistep approach for blockcave production scheduling optimization, International Journal of Mining Science and Technology23 (2013) 739–750.##[2]. Ataeepour, M. (2005). A critical survey of the existing stope layout optimization techniques, Journal of Mining Science, Vol. 41, No. 5: 447466.##[3]. Alford, C., Brazil, M. and Lee, D.H. (2007). Optimization in underground mining. Handbook of Operations Research in Natural Resources. Weintraub, A., Romero, C., Bjorndal, T., and Epstein, R. (eds.). Springer, New York. 561–577.##[4]. Riddle, J.M. (1977). A dynamic programming solution of a blockcaving mine layout. Proceedings of the Fourteenth International Symposium on the Application of Computers and Operations Research in the Mineral Industry, October 48, Society for Mining, Metallurgy and Exploration Inc., Colorado. 767– 780.##[5]. Ovanic, J. and Young, D.S. (1995). Economic optimization of stope geometry using separable programming with special branch and bound techniques. Third Canadian Conference on Computer Applications in the Mineral Industry. Balkema, Rotterdam. 129–135.##[6]. Serra, J.P. (1982). Image Analysis and Mathematical Morphology Academic Press, New York.##[7]. Deraisme, J., Fouquet, D.C. and Fraisse, H. (1984). Geostatistical ore body model for computer optimization of profits from different underground mining methods. Proceedings of the 18th International Conference on the Application of Computers and Operations Research in the Mining Industry (APCOM), London, England. 583–590.##[8]. Alford, C. (1996). Optimization in underground mine design. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts. 33 (5): 220A–220A.##[9]. Ataeepour, M. (2000). A heuristic algorithm to optimize stope boundaries. Ph.D. thesis, University of Wollongong, Australia.##[10]. Cheimanoff, N.M., Deliac, E.P. and Mallet, J.L. (1989). Geocad: an alternative CAD and artificial intelligence tool that helps moving from geological resources to mineable reserves. 21st International Symposium on the Application of Computers and Operations Research in the Mineral Industry. Society for Mining, Metallurgy and Exploration Inc., Colorado. 471–478.##[11]. Manchuk, J. and Deutsch, C. (2008). Optimizing stope designs and sequences in underground mines. SME Transactions, 324. 67–75.##[12]. Bai, X., Marcotte, D., Simon, R., (2014). A heuristic sublevel stope optimizer with multiple raises, The Journal of The Southern African Institute of Mining and Metallurgy. 114: 427–434.##[13]. Shenavar, M., Ataeepour, M. and Rahmanpour M., (2016). Evaluating mineable reserve in presence of grade uncertainty using floating stope optimizer in underground mines; 6th International Conference on Computer Applications in the Minerals Industries (CAMI), 2016, Istanbul, Turkey.##[14]. Williams, J.K., Smith, L. and Wells, P.M. (1973). Planning of underground copper mining.10th Internat. Appl. Sympos. Appl. Comput. Mineral Indust. (APCOM), Johannesburg, South Africa. 251–254.##[15]. Gillenwater, E.L., (1988). An integrated model for production planning and scheduling in underground coal mining, Doctor of Business dissertation, University of Kentucky##[16]. Chanda, E.K.C. (1990). An application of integer programming and simulation to production planning for a strati form ore body. Mining Sci. Tech. 11(2) 165–172.##[17]. Jawed, M. (1993). Optimal production planning in underground coal mines through goal programming: A case study from an Indian mine. J. Elbrond, X. Tang, eds. Proc. 24th Internat. Appl. Comput. Oper. Res. Mineral Indust. (APCOM) Sympos., CIM, Montréal, 44–50.##[18]. Winkler, B. (1998). System for quality oriented mine production planning with MOLP. Proc. 27th Internat. Appl. Comput. Oper. Res. Mineral Indust. (APCOM) Sympos., Royal School of Mines, London, 53–59.##[19]. Topal, E. (2003). Advanced underground mine scheduling using mixed integer programming. PhD thesis, Colorado School of Mines, Colorado.##[20]. Rahal, D., M., Smith, G., Van Hout, A. and Johannides, V. (2003). The use of mixed integer linear programming for longterm scheduling in block caving mines. F. CamisaniCalzolari, ed. Proc 31st Internat. Appl. Comput. Oper. Res. Mineral Indust. (APCOM) Sympos., SAIMM, Cape Town, South Africa, 123–131.##[21]. Rubio, E. and Diering, E. (2004). Block cave production planning using operation research tools. A. Karzulovic, M. Alfaro, eds. Proc. MassMin 2004, Instituto de Ingenieros de Chile, Santiago, Chile, 141–149.##[22]. Kuchta, M., Newman, A. and Topal. E. (2004). Implementing a production schedule at LKAB’s Kiruna Mine. Interfaces. 34 (2): 124–134.##[23]. Sarin, S.C. and J. WestHansen. (2005). The longterm mine production scheduling problem. IIE Trans. 37(2) 109–121.##[24]. Newman, A. and Kuchta, M. (2007). Using aggregation to optimize longterm production planning at an underground mine. Eur. J. Oper. Res. 176 (2): 1205–1218.##[25]. Carlyle, W.M. and Eaves. B.C. (2001). Underground planning at Stillwater mining company. Interfaces 31(4) 50–60.##[26]. McIsaac, G. (2005). Longterm planning of an underground mine using mixedinteger linear programming, CIM Bulletin, Vol. 98, No.1089, 1–6.##[27]. Fava, L., SaavedraRosas, J., Tough V. and Haarala, P. (2013). Heuristic optimization of scheduling scenarios for achieving strategic mine planning targets, the 23rd World Mining Congress, Montreal, Canada.##[28]. O’Sullivan, D. and Newman, A. (2015). Optimizationbased heuristics for underground mine scheduling, European Journal of Operational Research, Volume 241, Issue 1, Pages 248–259.##[29]. Magda, R. (1994). Mathematical model for estimating the economic effectiveness of production process in coal panels and an example of its practical application. Internat. J. Prod. Econom. 34 (1): 47–55.##[30]. Whitchurch, K., Cram, A.A., Ozawa, N. and Koizum, K. (1996). Underground and opencut coal scheduling using expert systems; 26th apcom proceedings, 339346.##[31]. Foroughi, S., Khademi, J., Monjezia, M. and Nehring, M. (2019). The integrated optimization of underground stope layout designing and production scheduling incorporating a nondominated sorting genetic algorithm (NSGAII), Resources Policy, Vol. 63, 101408.##[32]. Epstein, R., Gaete, S., Caro, F., Weintraub, A., Santibañez, P. and Catalan, J. (2003). Optimizing long term planning for underground copper mines. Proc. Copper 2003Cobre 2003, 5th Internat. Conf., Vol I, Santiago, Chile, CIM and the Chilean Institute of Mining, 265–279.##[33]. Marco Schulze and Jürgen Zimmermann. (2010). Scheduling in the Context of Underground Mining, Operations Research Proceedings, DOI 10.1007/9783642200090_96.##[34]. Copland, T. and Nehring, M. (2016). Integrated optimization of stope boundary selection and scheduling for sublevel stoping operations, J. S. Afr. Inst. Min. Metall. vol.116 (12): 11351142.##[35]. Little, J., Knights, P. and Topal, E. (2013). Integrated optimization of underground mine design and scheduling. J. S. Afr. Inst. Min. Metall 113 (10): 775–785.##[36]. Foroughi S., Khademi Hamidi, J., Monjezi, M. and Nehring, M. (2019). The integrated optimization of underground stope layout designing and production scheduling incorporating a nondominated sorting genetic algorithm (NSGAII), Resources Policy 63 (2019) 101408.##[37]. O’Sullivan, D., Newman, A., (2014), Extraction and Backﬁll Scheduling in a Complex Underground Mine, Interfaces 44(2), pp. 204–221, © 2014 INFORMS.##[38]. Brickey, A.J., (2015), Underground production scheduling optimization with ventilation constraints, PhD thesis of the Colorado School (Mining and Earth Systems Engineering).##[39]. Martinez MA, Newman AM (2011) A solution approach for optimizing long and shortterm production scheduling at LKAB’s Kiruna mine. Eur. J. Oper. Res. 211 (1):184–197.##[40]. Martinelli, R., Collard, J. and Gamache, M. (2019), Strategic planning of an underground mine with variable cutof‑ grades, Optimization and Engineering, published on line Dec. 2019, DOI: 10.1007/s11081019094796.##[41]. Manríquez, F., Pérez, J. and Morales, N. (2020). A simulation–optimization framework for short‑term underground mine production scheduling, Optimization and Engineering, published on line March 2020, DOI: 10.1007/s1108102009496w.##[42]. Huang, S., LI, G., BenAwuah, E., Afum, B.O. and Hu, N. (2020). A stochastic mixed integer programming framework for underground mining production scheduling optimization considering grade uncertainty, IEEE, 6, 24495 24505.##]
1

Dissolution of Nickel and Cobalt from IronRich Laterite Ores Using Different Organic Acids
http://jme.shahroodut.ac.ir/article_1777.html
10.22044/jme.2020.9564.1869
1
Due to the decreasing production of nickel and cobalt from sulfide sources, the Ni and Co extraction from the oxide ores (laterites) have become more prevalent. In this research work, the effects of calcination prior to leaching, acid concentration, percent solid, pH, and stirring speed on the nickel and cobalt recoveries from an ironrich laterite ore sample were investigated using different organic acids. Then the response surface methodology was implemented in order to optimize the various parameters. By the design of experiments, the compound optimal concentrations of the three different organic acids (gluconic acid: lactic acid: citric acid with a ratio of 1:2:3) were 3.18 M, and S/L = 0.1, pH = 0.5, and the stirring speed = 386 rpm. With the aid of kinetic studies, a temperature of 75 °C, and a test time of 120 minutes, the highest nickel and cobalt recoveries were 25.5% and 37.6%, respectively. In the optimal conditions, the contribution of the percent solids to the nickel recovery was the most and negative, after which the contribution of pH was negative, and finally, the acid concentration had a positive effect. In the optimal conditions, the acid concentration, pH, and solid content were, respectively, important in the cobalt recovery. The SEM results showed that the surface of feed and residue particles in the optimal conditions was not significantly different, and the laboratory data was fitted to a shrinking core model. The results obtained indicated that the reaction rate was controlled by the diffusion reaction at the particle surface, and the activation energies of 11.09 kJ/mol for nickel and 28.04 kJ/mol for cobalt were consistent with this conclusion
0

779
797


M.
Hosseini Nasab
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
Iran
marzieh.hosseini@ut.ac.ir


M.
Noaparast
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
Iran
noparast@ut.ac.ir


H.
Abdollahi
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
Iran
h_abdollahi@ut.ac.ir
Ironrich laterites
Ni
Co
Leaching
RSM
[[1]. BÜYÜKAKINCI, E. and TOPKAYA, Y.A. (2009). Extraction of nickel from lateritic ores at atmospheric pressure with agitation leaching. Hydrometallurgy 97: 3338.##[2]. Sahu, S., Kavuri, N. and Kundu, M. (2011). Dissolution kinetics of nickel laterite ore using different secondary metabolic acids. Braz. J. Chem. Eng. 28 (2): 251258.##[3]. Pawlowska, A., and Sadowski, Z. (2017). Influence of chemical and biogenic leaching on surface area and particle size of laterite ore. PHYSICOCHEM PROBL MI Journal 53 (2): 869877.##[4]. Valix, M., Thangavelu, V., Ryan, D. and Tang, J. (2009). Using halotolerant Aspergillus foetidus in bioleaching nickel laterite ore. IJEWM 3 (34): 253264.##[5]. Lv, X., Lv, W., You, Z., Lv, X., and Bai, Ch. (2018). Nonisothermal kinetics study on carbothermic reduction of nickel laterite ore. POWDER TECHNOL 340: 495501.##[6]. Kim, J., Dodbiba, G., Tanno, H., Okaya, K., Matsuo, S. and Fujita, T. (2010). Calcination of lowgrade laterite for concentration of Ni by magnetic separation. Miner. Eng. 23 (4): 282288.##[7]. Ilyas, S., Ranjan Srivastava, R., Kim, H., Ilyas, N. and Sattar, R. (2020). Extraction of nickel and cobalt from a laterite ore using the carbothermic reduction roastingammoniacal leaching process. Separation and Purification Technology 232: 115971.##[8]. Li, G.H., RAO, M.j., LI, Q., PENG, Z.W.and JIANG, T. (2010). Extraction of cobalt from laterite ores by citric acid in presence of ammonium bifluoride. T NONFERR METAL SOC 20 (8): 15171520.##[9]. Kapusta, J.P.T. (2006). Cobalt production and markets: A brief overview. JOM US. 58 (10): 3336.##[10]. Dong, L., Kyungho, P., Zhan, W. and Xueyi, G. (2010). Response surface design for nickel recovery from laterite by sulfationroastingleaching process. T NONFERR METAL SOC 19: 9296.##[11]. Mondal, S., Paul, B., Kumar, V., Singh, D.K., and Chakravartty, J.K. (2015). Parametric optimization for leaching of cobalt from Sukinda ore of lateritic origin – A Taguchi approach. SEP PURIF TECHNOL 156: 827–834.##[12]. Petrus, H.B.T.M., Wanta, K.C., Setiawan, H., Perdana, I., and Astuti, W. (2018). Effect of pulp density and particle size on indirect bioleaching of Pomalaa nickel laterite using metabolic citric acid. IOP Conf. Series: Materials Science and Engineering 285: 15.##[13]. Kursunoglu, S., and Kaya, M. (2016). Atmospheric pressure acid leaching of Caldag lateritic nickel ore. INT J MINER PROCESS. 150: 18.##[14]. Norgate, T. and Jahanshahi, S. (2011). Assessing the energy and greenhouse gas footprints of nickel laterite processing. Miner. Eng. 24 (7): 698707.##[15]. Meng, L., Qu, J., Guo, Q., Xie, K., Zhang, P., Han, L., Zhang, G. and Qi, T. (2015). Recovery of Ni, Co, Mn, and Mg from nickel laterite ores using alkaline oxidation and hydrochloric acid leaching. SEP PURIF TECHNOL 143: 80–87.##[16]. Alibhai, K., Dudeney, A.W.L., Leak, D.J., Agatzini, S. and Tzeferis, P. (1993). Bioleaching and bioprecipitation of nickel and iron from laterites. FEMS microbiology reviews 11 (13): 8795.##[17]. Tang, J. and Valix, M. (2004). Leaching of lowgrade nickel ores by fungi metabolic acids. In book: Separations Technology VI: New Perspectives on Very LargeScale Operations: 116. ##[18]. Simate, G.S., Ndlovu, S., and Walubita, L.F. (2010). The fungal and chemolithotrophic leaching of nickel lateritesChallenges and opportunities. Hydrometallurgy 103 (14): 150157.##[19]. Astuti, W., Hirajima, T., Sasaki, K. and Okibe, N. (2016). Comparison of effectiveness of citric acid and other acids in leaching of lowgrade Indonesian saprolitic ores. Miner. Eng. 85: 116.##[20]. Quast, K., Connor, J.N., Skinner, W., Robinson, D.J. and AddaiMensah, J. (2015). Preconcentration strategies in the processing of nickel laterite ores Part 1: Literature review. Minerals Engineering 79: 261–268.##[21]. Ma, B., Wang, Ch., Yang, W., Yin, F., and Chen Y. (2013). Screening and reduction roasting of limonitic laterite and ammoniacarbonate leaching of nickel–cobalt to produce a highgrade iron concentrate. Minerals Engineering 50–51: 106–113.##[22]. Pickles, C.A., Forster, J. and Elliott, R. (2014). Thermodynamic analysis of the carbothermic reduction roasting of a nickeliferous limonitic laterite ore. Minerals Engineering 65: 33–40.##[23]. Rao, M., Li, G., Zhang, X., Luo, J., Peng, Z. and Jiang, T. (2016). Reductive roasting of nickel laterite ore with sodium sulfate for FeNi production. Part I: Reduction/sulfidation characteristics. Separation Science and Technology 51 (8): 1408–1420.##[24]. Morcali, M.H., Tafaghodi Khajavi, L. and Dreisinger, D.B. 2017. Extraction of nickel and cobalt from nickeliferous limonitic laterite ore using borax containing slags. International Journal of Mineral Processing 167: 27–34.##[25]. Moskalyk, R.R. and Alfantazi, A.M. (2002). Nickel laterite processing and electrowinning practice. Minerals Engineering 15 (2002) 593–605.##[26]. Whittington, B. I. and Muir, D. (2000). Pressure Acid Leaching of Nickel Laterites: A Review, Mineral Processing and Extractive Metallurgy Review: An International Journal, 21 (6): 527599.##[27]. Harris, B., White, C., Jansen, M. and Pursell, D. (2006). A new approach to the high concentration chloride leaching of nickel laterites. Presented at ALTA Ni/Co 11 Perth, WA, May 1517.##[28]. Kyle, J. (2010). Nickel laterite processing technologies – In: ALTA 2010 Nickel/Cobalt/Copper Conference, 24  27 May, Perth, Western Australia.##[29]. Senanayake, G., Childs, J., Akerstrom, B.D., and Pugaev, D. (2011). Reductive acid leaching of laterite and metal oxides — A review with new data for Fe (Ni,Co)OOH and a limonitic ore. Hydrometallurgy 110: 13–32.##[30]. Thubakgale, C.K., Mbaya, R.K.K. and Kabongo, K. (2013). A study of atmospheric acid leaching of a South African nickel laterite. Minerals Engineering 54: 79–81.##[31]. Agacayak, T., Zedef, V. and Aras, A. (2016). Kinetic study on leaching of nickel from Turkish lateritic ore in nitric acid solution. J. Cent. South Univ. 23: 39−43.##[32]. Agacayak, T., and Aras, A. (2017). Dissolution kinetics of nickel from GÖRDES (ManisaTurkey) lateritic ore by sulphuric acid leaching under effect of sodium fluoride. J. Eng. Sci. Tech., 5 (3), 353361.##[33]. Coban, O., Baslayici, S. and Acma, M.E. (2018). Nickel and Cobalt Exraction from Caldag Lateritic Nickel Ores by Hydrometallurgical Processes. Conference Paper, UCTEA Chamber of Metallurgical & Materials Engineers’s Training Center.##[34]. Sahu, S., Kavuri, N.C. and Kundu, M. (2011). Dissolution kinetics of nickel laterite ore using different secondary metabolic acids. Brazilian Journal of Chemical Engineering 28 (2): 251  258.##[35] .MacCarthy, J., Nosrati, A., Skinner, W. and AddaiMensah, J. (2016). Atmospheric acid leaching mechanisms and kinetics and rheological studies of a low grade saprolitic nickel laterite ore. Hydrometallurgy 160: 2637.##[36]. Ghassa, S., Boruomand, Z., Abdollahi, H., Moradian, M. and Akcil A. (2014). Bioleaching of high grade ZnPb bearing ore by mixed moderate thermophilic microorganisms. SEP PURIF TECHNOL 136: 241249.##[37]. Ghassa, S., Gharabaghi, M., Azadmehr, A.R. and Nasrabadi, M. (2015). Effects of Flow Rate, Slurry Solid Content and Feed Size Distribution on Rod Mill Efficiency. PARTICUL SCI TECHNOL 34 (5): 533539.##[38]. Tang, J.A., and Valix, M. (2006). Leaching of low grade limonite and nontronite ores by fungi metabolic acids. Miner. Eng. 19 (12): 12741279.##[39]. Li, J., Li, X., Hu, Q., Wang, Z., Zhou, Y., Zheng, J., Liu, W. and Li, L. (2009). Effect of preroasting on leaching of laterite. Hydrometallurgy 99 (12): 8488.##[40]. Valix, M., Usai, F., and Malik, R. (2001). The electrosorption properties of nickel on laterite gangue leached with an organic chelating acid. Miner. Eng. 14 (2): 205215.##[41]. Wanta, K.C., Perdana, I., and Petrus, H.T.B.M. (2017). Evaluation of shrinking core model in leaching process of Pomalaa nickel laterite using citric acid as leachant at atmospheric conditions. Second International Conference on Chemical Engineering (ICCE), IOP Conf. Series: Materials Science and Engineering 162 (1).##[42]. Önal, M.A.R. and Topkaya, Y.A. (2014). Pressure acid leaching of Çaldağ lateritic nickel ore: an alternative to heap leaching. Hydrometallurgy 1 (42): 98107.##[43]. Chang, Y., Zhao, K. and Pešić, B. (2016). Selective leaching of nickel from prereduced limonitic laterite under moderate HPAL conditionsPart I: Dissolution. J MIN METALL B 52 (2): 127134.##[44]. Komesu, A., Martinez, P.F.M., Lunelli, B.H., Oliveira, J., Maciel, M.R.W., and Filho, R.M., Study of Lactic Acid Thermal Behavior Using Thermoanalytical Techniques. J. Chem: 17.##[45]. Tang, A., Su, L., Li, C., and Wei, W. (2010). Effect of mechanical activation on acidleaching of kaolin residue. Appl Clay Sci. 48 (3): 296299.##[46]. Garabaghi, M., Noaparast, M., and Irannajad, M. (2009). Selective leaching kinetics of lowgrade calcareous phosphate ore in acetic acid. Hydrometallurgy 95 (3): 341345.##[47]. Lima, P., Angelica, R. and Neves, R. (2014). Dissolution kinetics of metakaolin in sulfuric acid: Comparison between heterogeneous and homogeneous reaction methods. Appl Clay Sci. 88: 159162.##[48]. Ghassa, S., Noaparast, M., Shafaei, S.Z., Abdollahi, H., Gharabaghi, M. and Borumand, Z. (2017). A study on the zinc sulfide dissolution kinetics with biological and chemical ferric reagents. Hydrometallurgy 171: 362373.##[49]. MacCarthy, J., Nosrati, A., Skinner, W. and AddaiMensah, J. (2014). Atmospheric acid leaching of nickel laterite: Effect of temperature, particle size and mineralogy. Chemeca, Processing excellence; Powering our future, Western Australia, 1273.##[50]. Levenspiel, O. (1972). Chemical engineering reaction. WileyEastern Limited, New York.##[51] Habashi, F. (1999). Kinetics of metallurgical processes. Metallurgie Extractive Quebec.##[52]. Uçar, G. (2009). Kinetics of sphalerite dissolution by sodium chlorate in hydrochloric acid. Hydrometallurgy 95 (1): 3943.##]
1

A Correlation for Estimating LCPC Abrasivity Coefficient using Rock Properties
http://jme.shahroodut.ac.ir/article_1782.html
10.22044/jme.2020.9520.1863
1
Rock abrasivity, as one of the most important parameters affecting the rock drillability, significantly influences the drilling rate in mines. Therefore, rock abrasivity should be carefully evaluated prior to selecting and employing drilling machines. Since the tests for a rock abrasivity assessment require sophisticated laboratory equipment, empirical models can be used to predict rock abrasivity. Several indices based on five known methods have been introduced for assessing rock abrasivity including rock abrasivity index (RAI), Cerchar abrasivity index (CAI), Schimazek’s abrasivity factor (Fabrasivity), bit wear index (BWI), and LCPC abrasivity coefficient (LAC). In this work, 12 rock types with different origins were investigated using the uniaxial compressive strength (UCS), Brazilian test for tensile strength, and longitudinal wave velocity and LCPC tests, and microscopic observations were made to obtain a correlation for estimating the LCPC abrasivity coefficient by conducting the conventional rock mechanics tests. Using the equivalent quartz content, velocity of longitudinal waves, and rock brittleness index, a linear correlation was obtained with a coefficient of determination (R2) of 93.3% using SPSS in order to estimate LAC.
0

799
808


M.
Ansari
Department of Mining Engineering, Faculty of Technical and Engineering, Imam Khomeini International University, Qazvin, Iran
Iran
ansari.milad71@gmail.com


M.
Hosseini
Department of Mining Engineering, Faculty of Technical and Engineering, Imam Khomeini International University, Qazvin, Iran
Iran
mahdi_hosseini@ikiu.ac.ir


A. R.
Taleb Beydokhti
Department of Geology, Faculty of Science, Imam Khomeini International University, Qazvin, Iran
Iran
alireza93108@yahoo.com
Abrasivity index
Rock Properties
LCPC test
SPSS software
Statistical Analysis
[[1]. Osanloo, M. (1996). Drilling Methods. Sadra Publications.##[2]. Ersoy, A. and Waller, M.D. (1995). Textural characterization of rocks. J. of Engineering Geology, Vol. 39, No. 3. Pp.123136. DOI: 10.1016/00137952(95)00005Z.##[3]. Büchi, E. and WYSS, S. (1995). Gesteinsabrasivitätein bedeutender Kostenfaktor beim mechanischen Abbau von Festund Lockergestein. Tunnel, Vol. 14, No. 5. pp. 38–44.##[4]. Plinninger R., Kasling H., Thuro K. and Spaun G. (2003). Testing conditions and geomechanical properties influencing the Cerchar abrasiveness index (CAI) value. Technical note. Int J Rock Mech Min Sci, Vol. 40, No. 2, 259–263, DOI: 10.1016/S13651609(02)001405.##[5]. Deliormanl, A.H. (2012). Cerchar abrasivity index (CAI) and its relation to strength and abrasion test methods for marble stones. Construction and Building Materials, Vol. 30, pp. 16–21, DOI: 10.1016/j.conbuildmat.2011.11.023.##[6]. Thuro, K., Singer, J., Kasling, H. and Bauer, M. (2007). Determining abrasivity with the LCPC Test, In E. Eberhardt, D. Stead & T. Morrison (eds.). Proceedings of the 1st Canada–U.S. Rock Mechanics Symposium, 27.31.05.2007, Vancouver B.C., London: Taylor & Francis.##[7]. Tripathy, A., Singh, T.N. and Kundu, J. (2015). “Prediction of abrasiveness index of some Indian rocks using soft computing methods. “Measurement, Vol. 68, pp. 302–309, DOI: 10.1016/j.measurement.2015.03.009.##[8]. Moradizadeh, M., Cheshomi, A., Ghafoori, M. and Trigh Azali, S. (2016). Correlation of equivalent quartz content, Slake durability index and Is50 with Cerchar abrasiveness index for different types of rock. International Journal of Rock Mechanics & Mining Sciences, Vol. 86, pp. 42–47, DOI: 10.1016/j.ijrmms.2016.04.003.##[9]. Abu Bakar, M.Z., Majeed, Y. and Rostami, J. (2016). Effects of rock water content on CERCHAR Abrasivity Index. Wear, Vol. 368, pp.132–145, DOI: 10.1016/j.wear.2016.09.007.##[10]. Young, K., Kon, K., Son, Y. and Jeon, S. (2016). Effect of geomechanical properties on Cerchar Abrasivity Index (CAI) and its application to TBM tunneling. Tunnelling and Underground Space Technology, 57, 99111.##[11]. Kahraman, S., Fener, M., Käsling, H. and Thuro, K. (2016). The influences of textural parameters of grains on the LCPC abrasivity of coarsegrained igneous rocks. Tunnelling and Underground Space Technology, Vol. 58, pp. 216–223, DOI: 10.1016/j.tust.2016.05.011.##[12]. Capik, M. and Yilmaz, O. (2017). Correlation between Cerchar abrasivity index, rock properties and drill bit lifetime. Arab J Geosci, Vol. 10, No. 1.##[13]. Ataei, M. and Hosseini, S.H. (2008). “Investigation of the effect of abrasion on the rock drilling ability." Earth Sciences, Vol. 19, No. 74, pp. 137142.##[14]. Thuro, K. (1997). Drillability predictiongeological influence in hard rock drill and blast tunneling. Geol Runsch, Vol. 86, No. 2, pp. 426438, DOI: 10.1007/s005310050.##[15]. Ulusay, R. and Hudson, J.A. (2007). The complete ISRM suggested methods for rock characterization, testing and monitoring. ISRM Turkish National Group, Ankara, Turkey.##[16]. Ko, T.Y., Kim, T.K., Son, Y. and Jeon, S. (2016). Effect of geomechanical properties on Cerchar Abrasivity Index (CAI) and its application to TBM tunneling. Tunnelling and Underground Space Technology, Vol. 57, pp. 99111, DOI: 10.1016/j.tust.2016.02.006.##[17]. Hucka, V. and Das, B. (1974). “Brittleness determination of rocks by different methods. Int. J. Rock Mech. Min. Sci. Geomech. Abstr, Vol. 11, No. 10, pp. 389–392, DOI: 10.1016/01489062(74)911097.##[18]. Esmailian, M. and Rabiee, M.R (2015). “Comprehensive SPSS 22 help. Dibagaran Tehran Art & Cultural Institute, Tehran.##[19]. Taleb Beydokhti, A. (2014). Engineering Geological Characteristics of Karaj Formation Tuffs with an Emphasis on TimeDependent Behaviors (Weathering and Creep) from North Qazvin to North Tehran. Engineering Geology PhD Thesis, Department of Basic Sciences, BuAli Sina University, Hamedan, Iran.##]
1

A 3D FiniteDifference Analysis of Interaction between a NewlyDriven Large Tunnel with Twin Tunnels in Urban Areas
http://jme.shahroodut.ac.ir/article_1783.html
10.22044/jme.2020.9444.1851
1
Evaluation of the interaction between a new and the existing underground structures is one of the important problems in urban tunneling. In this work, using FLAC3D, four numerical models of single and twintube tunnels in urban areas are developed, where the horizontal distance between the single and twintube tunnels are varied. The aim is to analyze the effects of the horizontal distances, considering various criteria such as the deformation of linings, the forces and moments exerted on the twintube tunnels and their safety factors, the subsidence that occur on the surface and the nearby buildings, the stability of the singletube tunnel, and the stability of the pillar lying between the single and twintube tunnels. Considering the abovementioned criteria, the results obtained indicate that the interaction between the single and twintube tunnels is virtually negligible in the distance more than three times the singletube tunnel diameter. Also the stability of the pillar lying between the tunnels makes the distance to be chosen at least 1.5 times the singletube tunnel diameter.
0

809
823


S.
Akbari
Shahrood University of Technology, Faculty of Mining, Geophysics and Petroleum Engineering, Shahrood, Iran
Iran
akbari.sbr@gmail.com


Sh.
Zare
Shahrood University of Technology, Faculty of Mining, Geophysics and Petroleum Engineering, Shahrood, Iran
Iran
zare@shahroodut.ac.ir


H.
Chakeri
Sahand University of Technology, Dept. of Mining Engineering, Tebriz, Iran
Iran
chakeri@sut.ac.ir


H.
Mirzaei Nasir Abad
Sahand University of Technology, Dept. of Mining Engineering, Tebriz, Iran
Iran
hmirzaei@sut.ac.ir
3D Numerical Modeling
Interaction between tunnels
Flac3D
Soft ground tunneling
[[1]. Chakeri, H. and Ünver, B. (2014). A new equation for estimating the maximum surface settlement above tunnels excavated in soft ground. Environmental earth sciences. 71 (7): 31953210.##[2]. Soliman, E., Duddeck, H. and Ahrens, H. (1993). Twoand threedimensional analysis of closely spaced doubletube tunnels. Tunnelling and Underground Space Technology. 8 (1): 1318.##[3]. Kawata, T., Ohtsuka, M. and Kobayashi, M. (1993). Observational construction of largescaled twin road tunnels with minimum interval. In Infrastructures souterraines de transports (pp. 241248).##[4]. Perri, G. (1995). Analysis of the effects of the two new twin tunnels excavation very close to a big diameter tunnel of Caracas, subway. In International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts (Vol. 3, No. 32, p. 138A).##[5]. Yamaguchi, I., Yamazaki, I. and Kiritani, Y. (1998). Study of groundtunnel interactions of four shield tunnels driven in close proximity, in relation to design and construction of parallel shield tunnels. Tunnelling and Underground Space Technology. 13 (3): 289304.##[6]. Karakus, M., Ozsan, A. and Başarır, H. (2007). Finite element analysis for the twin metro tunnel constructed in Ankara Clay, Turkey. Bulletin of Engineering Geology and the Environment. 66 (1): 7179.##[7]. Liu, H.Y., Small, J.C., Carter, J.P. and Williams, D.J. (2009). Effects of tunnelling on existing support systems of perpendicularly crossing tunnels. Computers and Geotechnics. 36 (5): 880894.##[8]. Addenbrooke, T.I. and Potts, D.M. (2001). Twin tunnel interaction: surface and subsurface effects. International Journal of Geomechanics. 1 (2): 249271.##[9]. Chehade, F.H. and Shahrour, I. (2008). Numerical analysis of the interaction between twintunnels: Influence of the relative position and construction procedure. Tunnelling and Underground Space Technology. 23 (2): 210214.##[10]. Chakeri, H., Hasanpour, R., Hindistan, M.A. and Ünver, B. (2011). Analysis of interaction between tunnels in soft ground by 3D numerical modeling. Bulletin of Engineering Geology and the Environment. 70 (3): 439448.##[11]. Sarfarazi, V., Haeri, H., Safavi, S., Marji, M.F. and Zhu, Z. (2019). Interaction between two neighboring tunnel using PFC2D. Structural Engineering and Mechanics. 71 (1): 7787.##[12]. Abdollahi, M.S., Najafi, M., Bafghi, A.Y. and Marji, M.F. (2019). A 3D numerical model to determine suitable reinforcement strategies for passing TBM through a fault zone, a case study: Safaroud water transmission tunnel, Iran. Tunnelling and Underground Space Technology, 88, 186199.##[13]. Mirsalari, S.E., Fatehi Marji, M., Gholamnejad, J. and Najafi, M. (2017). A boundary element/finite difference analysis of subsidence phenomenon due to underground structures. Journal of Mining and Environment. 8 (2): 237253.##[14]. Lambrughi, A., Rodríguez, L.M. and Castellanza, R. (2012). Development and validation of a 3D numerical model for TBM–EPB mechanised excavations. Computers and Geotechnics, 40, 97113.##[15]. Structurepoint (2012) spColumn Manual. Skokie, USA.##[16]. CarranzaTorres, C. and Diederichs, M. (2009). Mechanical analysis of circular liners with particular reference to composite supports. For example, liners consisting of shotcrete and steel sets. Tunnelling and Underground Space Technology. 24 (5): 506532.##[17]. Sakurai S (1997) Lessons learned from field measurements in tunneling. Tunn Undergr Sp Tech 12 (4): 453460. doi: 10.1016/S08867798(98)000042.##[18]. Rankin, W (1988) Ground Movements Resulting from Urban Tunnelling: Predictions and Effects. Geological Society London Engineering Geology Special Publications. 5 (1):7992. doi: 10.1144/GSL.ENG.1988.005.01.06.##[19]. Guglielmetti, V., Grasso, P., Mahtab, A. and Xu, S. (Eds.). (2008). Mechanized tunnelling in urban areas: design methodology and construction control. CRC Press.##]
1

Copper Price Prediction using Wave Count with Contribution of Elliott Waves
http://jme.shahroodut.ac.ir/article_1786.html
10.22044/jme.2020.9240.1822
1
Within the last few decades, copper has been identified as one of the most applicable metals by many researchers. These researchers have also been enthusiastic to predict the price of this valuable metal. These days, the available technical analysis methods have been highly applied in the financial markets. Moreover, the researchers have used these methods to predict the suitable price trends. In the present work, some technical analysis tools including the Fibonacci series, Elliott waves, and Ichimuko clouds were practiced to scrutinize the price changes and predict the copper price. All copper prices from 2008 to 2016 were considered. Regarding the fractal property of these methods, the relations among prices were obtained within an eightyear time sequence. Since 2016, the copper price has been gradually deviated from its previous trend. Using the wave count and Elliott waves has specified that the wave number 1 and wave number 2 have been completed. Now, the time has come to develop the wave number 3. According to the relations introduced by the Elliott waves and the clouds made by Ichimiku, it was determined that the copper price would be almost $16000 per ton in 2022.
0

825
835


R.
Satari
Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Iran
ramin.sattari@gmail.com


A.
Akbari Dehkharghani
Department of Petroleum, Mining and Material Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
Iran
afshinkr@gmail.com


K.
Ahangari
Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Iran
kaveh.ahangari@gmail.com
Copper
Metal Price Prediction
Elliott Waves
[[1]. Atsalakis, G.S., Dimitrakakis, E.M. and Zopounidis, C.D. (2011). Elliott Wave Theory and neurofuzzy systems, in stock market prediction: The WASP system. Expert Systems with Applications. 38 (8): 91969206.##[2]. Tirea, M., Tandau, I., & Negru, V. (2012, August). Stock market multiagent recommendation system based on the elliott wave principle. In International Conference on Availability, Reliability, and Security (pp. 332346). Springer, Berlin, Heidelberg.##[3]. Magazzino, C., Mele, M. and Prisco, G. (2012). The Elliott's Wave Theory: Is it True During the Financial Crisis. The Elliott’s Wave Theory: Is It True During the Financial Crisis, 100108.##[4]. Volna, E., Kotyrba, M. and Jarušek, R. (2013). Prediction by means of Elliott waves recognition. In Nostradamus: Modern Methods of Prediction, Modeling and Analysis of Nonlinear Systems (pp. 241250). Springer, Berlin, Heidelberg.##[5]. E. Volna, M. Kotyrba, R. Jarusek, (2013), Multiclassifier based on Elliott wave’s recognition, Computers and Mathematics with Applications 66, 213–225.##[6]. Volná, E., Kotyrba, M., Oplatková, Z.K. and Senkerik, R. (2018). Elliott waves classification by means of neural and pseudo neural networks. Soft computing. 22 (6): 18031813.##[7]. Ilalan, D. (2016). Elliott wave principle and the corresponding fractional Brownian motion in stock markets: Evidence from Nikkei 225 index. Chaos, Solitons & Fractals. 92: 137141.##[8]. Vishvaksenan, K.S., Kalaiarasan, R., Kalidoss, R. and Karthipan, R. (2018). Real time experimental study and analysis of Elliott wave theory in signal strength prediction. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences. 88 (1): 107119.##[9]. Vantuch, T., Zelinka, I. and Vasant, P. (2016). Market Prices Trend Forecasting Supported By Elliott Wave’s Theory. In 1st EAI International Conference on Computer Science and Engineering (p. 335). European Alliance for Innovation (EAI).##[10]. Vantuch, T., Zelinka, I. and Vasant, P. (2018). An algorithm for Elliott Waves pattern detection. Intelligent Decision Technologies. 12 (1): 1524.##[11]. Marañon, M. and Kumral, M. (2018). Exploring the Elliott Wave Principle to interpret metal commodity price cycles. Resources Policy. 59: 125138.##[12]. Frost, A.J. and Prechter, R.R. (1995). Elliott wave principle: key to market behavior. New Classics Library.##[13]. Teseo, R. (2001). The ElliottFibonacci connection. Futures. 30 (13): 5252.##[14]. Marañon, M. and Kumral, M. (2018). Exploring the Elliott Wave Principle to interpret metal commodity price cycles. Resources Policy, 59, 125138.##[15]. Prechter Jr, R.R. and Bolton, A.H. (1994). The Complete Elliott Wave Writings of A. Hamilton Bolton. Elliott Wave International.##[16]. Espinal, J.C.C. and Méndez, E.R.J. (2001). Ondas de Elliot: La clave para obtener excelentes beneficios en el mercado de valores. INNOVAR. Revista de Ciencias Administrativas y Sociales. (18) : 920.##[17]. Balan, R. (1989). Elliott Wave Principle Applied to the Foreign Exchange Markets. BBS Publication.##[18].https://www.lme.com /enGB/Metals /Nonferrous /Copper#tabIndex=2.##]
1

Mechanical Properties of Low Plasticity Clay Soil Stabilized with Iron Ore Mine Tailing and Portland Cement
http://jme.shahroodut.ac.ir/article_1790.html
10.22044/jme.2020.9304.1860
1
Due to economical and environmental issues, utilization of mineral wastes, e.g. iron ore mine tailing (IOMT), as road materials can be recommended as a sustainable alternative. In the present study, mechanical properties, as well as resistance to freezing and thawing cycles (FT) of low plasticity clay soil stabilized with different percentages of Portland cement (0, 6, 9, 12 and 15%) and different IOMT content (0, 10, 20, 30 and 40%) has been investigated. To this end, unconfined compressive strength (UCS), initial elastic modulus (E0), and indirect tensile strength (ITS) at different curing times of 7, 14, 18, and 56 days for different admixtures was determined to select optimum mix design for stabilization of clayey subgrade soil. This study shows that by increasing the percentage of cement, strength parameters such as UCS, E0, and ITS increases while increasing IOMT does not show a specific trend to increase strength parameters. Evaluation of strength parameters at different curing time showed that in shortterm curing times (7 and 14 days), iron ore mine tailing has a positive effect on the strength parameters, while in longterm curing times (28 and 56 days), iron ore mine tailing has a negative effect on the strength parameters. In total, it was found that 12% of the Portland cement and 10 to 40% of the IOMT passes the UCS and FT criteria for stabilization of low plasticity clay soils, while clay soil (without IOMT) requires at least 15% of Portland cement for stabilization.
0

837
853


A. R.
Ghanizadeh
Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran
Iran
ghanizadeh@sirjantech.ac.ir


A.
Yarmahmoudi
Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran
Iran
abouzar.yarmahmoudi@yahoo.com


H.
Abbaslou
Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran.
Iran
abbaslou@sirjantech.ac.ir
Clay Soil
Iron Ore Mine Tailing
Portland Cement
Soil Stabilization
[[1]. Kossoff, D., Dubbin, W.E., Alfredsson, M., Edwards, S.J., Macklin, M.G. and HudsonEdwards, K.A. (2014). Mine tailings dams: characteristics, failure, environmental impacts, and remediation. Applied Geochemistry, 51:229245.##[2]. Kuranchie, F.A. (2015). Characterisation and applications of iron ore tailings in building and construction projects. PhD dissertation, School of Engineering, Faculty of Health, Engineering and Science.##[3]. Motz, H. and Geiseler, J. (2001). Products of steel slags: an opportunity to save natural resources. Waste management. 21 (3):285293.##[4]. Basha, E.A., Hashim, R., Mahmud, H.B. and Muntohar, A.S. (2005). Stabilization of residual soil with rice husk ash and cement. Construction and Building Materials. 19 (6):448453.##[5]. Wang, T., Liu, J. and Tian, Y. (2010). Experimental study of the dynamic properties of cement and limemodified clay soils subjected to freeze–thaw cycles. Cold Regions Science and Technology. 61 (1): 2933.##[6]. Kumar, A. and Gupta, D. (2015). Behavior of cementstabilized fiberreinforced pond ash, rice husk ashe soil mixtures. Geotextiles and Geomembranes. 44 (3):466474##[7]. Solanki, P., Zaman, M., and Khalife, R. (2013). Effect of FreezeThaw Cycles on Performance of Stabilized Subgrade, GeoCongress 2013, San Diego, California, United States, 567581.##[8]. Shibi, T. and Kamei, T. (2014). Effect of freeze–thaw cycles on the strength and physical properties of cementstabilised soil containing recycled bassanite and coal ash. Cold Regions Science and Technology. 106107: 3645.##[9]. Negi, A.S., Faizan, M., Siddharth, D.P. and Singh, R. (2013). Soil stabilization using lime. International Journal of Innovative Research in Science, Engineering and Technology. 2 (2): 448453.##[10]. Yang, Q. (2008). Study on Road Performance of Iron Tailing Sand Stabilized by Inorganic Binder. Dalian university of technology.##[11]. Sun, J. and Chen, C. (2012). Research on the performances of lime fly ash stabilized iron tailing gravel in highway application. Journal of Wuhan University of Technology, 34 (3):5962.##[12]. Manjunatha, L.S and Sunil, B.M. (2013). Stabilization/solidification of iron ore mine tailings using cement, lime and fly ash. International Journal of Research in Engineering and Technology. 2 (12):625635.##[13]. Xu, S. (2013). Research on Application of Iron Tailings on Road Base. Advanced Materials Research. 743:5457.##[14]. Hongbin, L. (2014). Experimental Research on Performance of Road Base with Cement Stabilized Iron Tailings Sand. Applied Mechanics and Materials. 513517:6064.##[15]. Osinubi, K.J., Yohanna, P. and Eberemu, A.O. (2015). Cement modification of tropical black clay using iron ore tailings as admixture. Transportation Geotechnics, 5:3549.##[16]. Bastos, C.A.L., Silva, C.G., Mendes, C.J. and Peixoto, F.A.R. (2016). Using Iron Ore Tailings from Tailing Dams as Road Material. Journal of Materials in Civil Engineering. 28 (10):04016102.##[17]. Etim, R.K., Eberemu, A.O. and Osinubi, K.J. (2017). Stabilization of black cotton soil with lime and iron ore tailings admixture. Transportation Geotechnics, 10:8595.##[18]. Jahanshahi, R., Zare, M. and Schneider, M. (2014). A metal sorption/desorption study to assess the potential efficiency of a tailings dam at the Golgohar Iron Ore Mine, Iran. Mine Water and the Environment. 33 (3):228240.##[19]. ASTM D3282. (2015). Standard Classification of Soils and SoilAggregate Mixtures for Highway Construction Purposes. West Conshohocken, PA, United States.##[20]. ASTM D2487. (2015). Standard Practice for Classification of Soils for Engineering Purposes (Unified Soil Classification System). West Conshohocken, PA, United States.##[21]. ASTM D854. (2014). Standard Test Methods for Specific Gravity of Soil Solids by Water Pycnometer. West Conshohocken, PA, United States.##[22]. ASTM D4318. (2014). Standard Test Methods for Liquid Limit, Plastic Limit, and Plasticity Index of Soils. West Conshohocken, PA, United States.##[23]. ASTM D427. (2008). Standard Test Method for Shrinkage Factors of Soils by the Mercury Method. West Conshohocken, PA, United States.##[24]. ASTM D4972. (2001). Standard test method for pH of soils. West Conshohocken, PA, United States.##[25]. ASTM D1557. (2012). Standard test methods for laboratory compaction characteristics of soil using modified effort. West Conshohocken, PA, United States.##[26]. ASTM D2166. (2015). Standard test method for unconfined compressive strength of cohesive soil. West Conshohocken, PA, United States.##[27]. Dexter, A. and Kroesbergen, B. (1985). Methodology for determination of tensile strength of soil aggregates. Journal of Agricultural Engineering Research. 31 (2):139147.##[28]. ASTM C496. (2014). Standard test method for splitting tensile strength of cylindrical concrete specimens. West Conshohocken, PA, United States.##[29]. ASTM D1883. (2016). Standard test method for California bearing ratio (CBR) of laboratorycompacted soils. West Conshohocken, PA, United States.##[30]. ASTM D560. (2016). Standard Test Methods for Freezing and Thawing Compacted SoilCement Mixtures. West Conshohocken, PA, United States.##[31]. Sridharan, A. and Nagaraj, H.B. (2005). Plastic limit and compaction characteristics of finegrained soils. Proceedings of the institution of civil engineersground improvement. 9 (1):1722.##[32]. Texas Department of Transportation. Cement Treatment. ITEM 2762014.##[33]. I.M. Syed. (2007). FullDepth Reclamation with Portland Cement: A Study of Long Term Performance. Portland Cement Association.##[34]. Morian, D.A., Solaimanian, M., Scheetz, B. and Jahangirnejad, S. (2012). Developing Standards and Specifications for Full Depth Pavement Reclamation. Commonwealth of Pennsylvania Department of Transportation, USA, Harrisburg.##[35]. Kumar, B.N.S., Suhas, R., Santosh, U.S. and Srishaila. (2014). Utilization of Iron Ore Tailings as Replacement to Fine Aggregates in Cement Concrete Pavements. International Journal of Research in Engineering and Technology. 3 (7): 369376.##[36]. Biswal, D. R., Sahoo, U. C., & Dash, S. R. (2018). Mechanical characteristics of cement stabilised granular lateritic soils for use as structural layer of pavement. Road Materials and Pavement Design, 123.##[37]. Diambra, A., Festugato, L., Ibraim, E., da Silva, A.P. and Consoli, N.C. (2018). Modelling tensile/ compressive strength ratio of artificially cemented clean sand. Soils and foundations. 58 (1): 199211.##[38]. Baldovino, J.D. J.A., dos Santos Izzo, R. L., Feltrim, F. and da Silva, É.R. (2020). Experimental Study on Guabirotuba’s Soil Stabilization Using Extreme Molding Conditions. Geotechnical and Geological Engineering, 38: 2591–2607.##[39]. Chan, C. M. (2012). Strength and stiffness of a cementstabilised lateritic soil with granulated rubber addition. Proceedings of the Institution of Civil EngineersGround Improvement. 165 1):4152.##[40]. Biswal, D.R., Sahoo, U.C. and Dash, S.R. (2017). Strength and stiffness studies of cement stabilized granular lateritic soil. In International Congress and Exhibition" Sustainable Civil Infrastructures: Innovative Infrastructure Geotechnology" (pp. 320336). Springer, Cham.##]
1

Behavior of a Tunnel and its Neighboring Joint with and without Presence of Rock Bolt under Biaxial Loads; Particle Flow Code Approach
http://jme.shahroodut.ac.ir/article_1813.html
10.22044/jme.2020.9581.1872
1
In this work, the interaction between the semicircular space and the neighboring joint with and without the presence of rock bolts was investigated using the particle flow code (PFC) approach. For this purpose, firstly, the calibration of PFC was performed using both the Brazilian experimental test and the uniaxial compression test. Secondly, a numerical model with the dimension of 100 mm * 100 mm was prepared. A semicircular space with a radius of 25 mm was situated below the model. A joint with a length of 40 mm was situated above the space. The joint opening was 2 mm. The joint angles related to the horizontal direction were 0°, 15°, 30°, 45°, 60°, and 75°. Totally, 6 different configurations of the semicircular space and neighboring joint were prepared. These models were tested with and without the presence of vertical rock bolts by the biaxial test. The rock bolt length was 50 mm. The value of the lateral force was fixed at 2 MPa. An axial force was applied to the model till the final failure occurred. The results obtained showed that the presence of rock bolts changed the failure pattern of the numerical model. In the absence of rock bolts, two tensile wing cracks initiated from the joint tip and propagated diagonally till coalescence from the model boundary. Also several shear bands were initiated in the left and right sides of the tunnel. In the presence of rock bolts, several shear bands were initiated in the left and right sides of the tunnel. The compressive strength with the presence of rock bolts was more than that without the presence of rock bolts. The failure stress had a minimum value when the joint angle was 45°.
0

855
864


V.
Sarfarazi
Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran
Iran
vahab.sarfarazi@gmail.com
PFC2D
Tunnel
Joint
rock bolt
tensile crack
[[1]. Indraratna, B. and Kaiser, P.K. (1990) Analytical model for the design of grouted rock bolts". International Journal for Numerical and Analytical Methods in Geomechanics. 14 (4): 227–251. ##[2]. Li, C. and Stillborg, B. (1999) Analytical models for rock bolts. International Journal of Rock Mechanics and Mining Sciences, 36(8):1013–1029.##[3]. Li, C.C. (2017) Principles of rockbolting design. Journal of Rock Mechanics and Geotechnical Engineering, 9(3), pp. 396–414. 2017.##[4]. Zhao, H., Ru, Z. and Zhu, C. (2018) Reliabilitybased Support Optimization of Rockbolt Reinforcement around Tunnels in Rock Masses. Periodica Polytechnica Civil Engineering. 62 (1): 250–258. ##[5]. Oreste, P. (2005) A probabilistic design approach for tunnel supports. Computers and Geotechnics, 32(7): 520–534.##[6]. Sharan, S.K. (2005) Exact and approximate solutions for displacements around circular openings in elasticbrittleplastic HoekBrown rock. International Journal of Rock Mechanics and Mining Sciences. 42 (4) 542– 549.##[7]. Sharan, S.K. (2008) Analytical solutions for stresses and displacements around a circular opening in a generalized HoekBrown rock. International Journal of Rock Mechanics and Mining Sciences. 45 (1): 78–85.##[8]. Hoek, E. and Brown, E.T. (1980) Underground Excavations in Rock. The Institution of Mining and Metallurgy, London.##[9]. Brown, E.T., Bray, J.W., Ladanyi, B. and Hoek, E. (1983) Ground reponse curves for rock tunnel. Journal of Geotechnical Engineering. 109 (1): 15– 39.##[10]. Cai, Y., Esaki, T. and Jiang, Y. (2004) An analytical model to predict axial loading routed rock bolt for soft rock tunneling. Tunnelling and Underground Space Technology. 19 (6): 607–618. ##[11]. Guan, Zh., Jiang, Y., Tanabasi, Y. and Huang, H.W. (2007) Reinforcement mechanics of passive bolts in conventional tunnelling. International Journal of Rock Mechanics and Mining Sciences: 44 (4). pp. 625–636.##[12]. Oreste, P. (2008) Distinct analysis of fully grouted bolts around a circular tunnel considering the congruence of displacements between the bar and the rock. International Journal of Rock Mechanics and Mining Sciences, 45(7): 1052–1067.##[13]. Indraratna, B. and Kaiser, P.K. (1990) Design for grouted rock bolts based on the convergence control method. International Journal of Rock Mechanics and Mining Sciences. 27 (4): 269–281.##[14]. Fahimifar, A. and Soroush, H. (2005) A theoretical approach for analysis of the interaction between grouted rockbolts and rock masses. Tunnelling and Underground Space Technology. 20 (4):333–343.##[15]. CarranzaTorres, C. (2009) Analytical and numerical study of the mechanics of rockbolt reinforcement around tunnels in rock masses. Rock Mechanics and Rock Engineering. 42 (2): 175–228.##[16]. Bobet, A. and Einstein, H.H. (2011) Tunnel reinforcement with rockbolts. Tunnelling and Underground Space Technology. 26 (1):100–123.##[17]. Mollon, G., Daniel, D. and Abdul, H.S. (2009) Probabilistic analysis of circular tunnels in homogeneous soil using response surface methodology. Journal of Geotechnical and Geoenvironmental Engineering. 135 (9):1314–1325.##[18]. Li, H. Z. and Low, B.K. (2010) Reliability analysis of circular tunnel under hydrostatic stress field. Computers and Geotechnics. 37 (1–2): 50–58.##[19]. Lu, Q. and Low, B.K. (2011) Probabilistic analysis of underground rock excavations using response surface method and SORM. Computers and Geotechnics. 38 (8):1008–1021.##[20]. Su, Y.H., Li, X. and Xie, Z.Y. (2011) Probabilistic evaluation for the implicit limitstate function of stability of a highway tunnel in China. Tunnel and Underground Space Technology. 26 (2), pp. 422–434.##[21]. Zhao, H., Ru, Z., Chang, X., Yin, S. and Li, S. (2014) Reliability analysis of tunnel using least square support vector machine. Tunnel and Underground Space Technology, 41:14–23.##[22]. Hoek, E. (1998) Reliability of HoekBrown estimates of rock mass properties and their impact on design. International Journal of Rock Mechanics and Mining Sciences. 35 (1): 63–68.##[23]. Zhang, W. and Goh, A.T.C. (2012) Reliability assessment on ultimate and serviceability limit states and determination of critical factor of safety for underground rock caverns. Tunnel and Underground Space Technology, 32: 221–230.##[24]. Itasca Consulting Group Inc, (2008) PFC2D (Particle Flow Code in 2D) Theory and Background,” Minneapolis, Minn, USA.##[25]. Potyondy, D.O. and Cundall, P.A. (2004) A bondedparticle model for rock, International Journal of Rock Mechanics and Mining Sciences, Vol. 41, No. 8, pp.1329–1364, 2004.##[26]. Donze, F.V., Richefeu, V. and Magnier S.A. (2009) Advances in discrete element method applied to soil rock and concrete mechanics. Electronic Journal of Geological Engineering 8: 144.##]
1

Integrating Geophysical Attributes with New Cuckoo Search MachineLearning Algorithm to Estimate Silver Grade Values–Case Study: Zarshouran Gold Mine
http://jme.shahroodut.ac.ir/article_1857.html
10.22044/jme.2020.9939.1923
1
The exploration methods are divided into the direct and indirect categories. Among these, the indirect geophysical methods are more time and costeffective compared with the direct methods. The target of the geophysical investigations is to obtain an accurate image from the underground features. The Induced polarization (IP) is one of the common methods used for metal sulfide ore detection. Since metal ores are scattered in the host rock in the Zarshouran mine area, IP is considered as a major exploration method. Parallel to IP, the resistivity data gathering and processing are done to get a more accurate interpretation. In this work, we try to integrate the IP/RS geophysical attributes with borehole grade analyses and geological information using the cuckoo search machinelearning algorithm in order to estimate the silver grade values. The results obtained show that it is possible to estimate the grade values from the geophysical data accurately, especially in the areas without drilling data. This reduces the costs and time of the exploration and ore reserves estimation. Comparing the results of the intelligent inversion with the numerical methods, as the major tools to invert the geophysical data to the ore model, demonstrate a superior correlation between the results.
0

865
879


A.
Alimoradi
Department of Mining Engineering, Imam Khomeini International University, Ghazvin, Iran
Iran
alimoradi@eng.ikiu.ac.ir


B.
Maleki
Department of Mining Engineering, Imam Khomeini International University, Ghazvin, Iran
Iran
maleki@eng.ikiu.ac.ir


A.
Karimi
Department of Mining Engineering, Imam Khomeini International University, Ghazvin, Iran
Iran
karimiahmad110@gmail.com


M.
Sahafzadeh
Mining plus company, Vancouver, Canada
Canada
maryam.sahafzadeh@miningplus.com


S.
Abbasi
Zarshouran gold mines and mineral industries development company, Tekab, Iran
Iran
saedabbasi1391@gmail.com
IP/RS attributes
Cuckoo search
MachineLearning
Zarshouran Deposit
Numerical methods
[[1]. Alimoradi, A. (2006). A comparison between RMR values of TSP203 and the real values. MSc. Thesis in Mine Exploration Engineering (Third Chapter), Shahrood University of Technology, 4564.##[2]. Alimoradi, A., Moradzadeh, A., Naderi, R., Zad Salehi, M. and Etemadi, A. (2008). Prediction of geological hazardous zones in front of a tunnel face using TSP203 and artiﬁcial neural networks, Tunnelling and Underground Space Technology, 23, 711717.##[3]. Alimoradi, A., Angorani, S., Ebrahimzadeh, M. and Shariat Panahi, M. (2011). Magnetic inverse modelling of a dike using the artificial neural network approach, Near Surface Geophysics, 9, 339347.##[4]. Bishop, C.M. (1995). Neural networks for pattern recognition, 1st edition, Oxford Clarendon.##[5]. Brown, W.M., Gedeon, T.D., Groves, D.I. and Barnes, R.G. (2000). Artificial neural networks: A new method for mineral prospectivity mapping, Auatrailian Journal of Earth Science, 47, 757770.##[6]. Brown, W.M., Gedeon, T.D. and Groves, D.I. (2003). Use of noise to augment training data: A neural network method of mineral potential mapping in regions of limited known deposit examples, Journal of Natural Resource Research, 12, 141152.##[7]. CalderónMacías, C., Sen, M.K. and Stoffa, P.L. (2001). Artificial neural networks for parameter estimation in geophysics, Geophysical Prospecting, 48, 21–47.##[8]. Demuth, H., Beale, M. (2002). Neural network toolbox for use with MATLAB, Version 3.0.##[9]. Douglas, W., Oldenburg, Yaoguo, Li. (1999). Estimating depth of investigation in dc resistivity and IP surveys, Geophysics, 64, 403416.##[10]. ElQady, G., Ushijima, K. (2001). Inversion of DC resistivity data using neural networks, Geophysical Prospecting, 49, 417430.##[11]. Hagan, M.T., Demuth, H.B. and Beale, M. (1996). Neural network design, PWS Publishing Company, Boston, MA.##[12]. Hasani Pak, A., Shojaat, B. (2000). Metalnonmetal ore modeling and their exploration application, University of Tehran.##[13]. Hosseinali, F. and Alesheikh, A.A. (2008). Weighting spatial information in GIS for copper mining exploration, Journal of Applied Science, 5, 11871198.##[14]. Loke, M. H. (1999). Electrical imaging surveys for environmental and engineering studies: A practical guide to 2D and 3D surveys, 14.##[15]. Nazri, M.N., Abdullah Khan, M.Z.R. (2013). A new Levenberg Marquardt based back propagation algorithm trained with Cuckoo search, Procedia Technology, 11, 1823.##[16]. Porwal, A. (2006). Mineral potential mapping with mathematical geological models, PhD thesis, University of Utrecht.##[17]. Poulton, M., ElFouly, A. (1991). Preprocessing GPR signatures for cascading neural network classification, 61st SEG meeting, Houston, USA, Expanded Abstracts 507–509.##[18]. Poulton, M., Sternberg, K., and Glass, C. (1992). Neural network pattern recognition of subsurface EM images, Journal of Applied Geophysics, 29, 21–36.##[19]. Sanchez, J.P., ChicaOlmo, M., and AbarcaHernandez, F. (2003). Artificial neural network as a tool for mineral potential mapping with GIS, Journal of Remote Sensing, 24, 11511156.##[20]. Selley, R.C., Cocks, R.M. and Plimer, I.R. (2005). Encyclopedia of geology, Vol. 1, 1st edition, Elsevier Ltd, Oxford.##[21]. Skabar, A.A. (2005). Mapping mineralization probabilities using multilayer perceptrons, Journal of Natural Resource Research, 14, 109123.##[22]. Singer, D.A. and Kouda, R.A. (1997). Classification of mineral deposit into types using mineralogy with a probabilistic neural network, Nonrenewable Resources, 6, 2732.##[23]. Singer, D.A. and Kouda, R.A. (1999). Comparison of the weightsofevidence method and probabilistic neural networks, Natural Resources Research, 8, 287298.##[24]. Singh, U.K., Tiwari, R.K. and Singh, S.B. (2005). Onedimensional inversion of geoelectrical resistivity sounding data using artificial neural networks – a case study, Computational Geoscience, 31, 99– 108.##[25]. Spichak, V.V., Popova, I.V. (2000). Artificial neural network inversion of MT – data in terms of 3D earth macro – parameters, Geophysical Journal International, 42, 15–26.##[26]. Yang, X.S. and Deb, S. (2010). Engineering optimization by Cuckoo Search, International Journal of Mathematical Modelling and Numerical, 1, 330343.##[30]. Yuval, Douglas, W., Oldenburg. (1995). DC resistivity and IP methods in acid mine drainage problems: results from the Copper Cliff mine tailings impoundments, Journal of Applied Geophysics, 34, 187198.##]
1

Investigating and Ranking Blasting Patterns to Reduce Ground Vibration using Soft Computing Approaches and MCDM Technique
http://jme.shahroodut.ac.ir/article_1833.html
10.22044/jme.2020.9446.1852
1
The blasting method is one of the most important operations in most openpit mines that has a priority over the other mechanical excavation methods due to its costeffectiveness and flexibility in operation. However, the blasting operation, especially in surface mines, imposes some environmental problems including the ground vibration as one of the most important ones. In this work, an evaluation system is provided to study and select the best blasting pattern in order to reduce the ground vibration as one of the hazards in using the blasting method. In this work, 45 blasting patterns used for the Sungun copper mine are studied and evaluated to help determine the most suitable and optimum blasting pattern for reducing the ground vibration. Additionally, due to the lack of certainty in the nature of ground and the analyses relating to this drilling system, in the first step, a combination of the imperialist competitive algorithm and kmeans algorithm is used for clustering the measured data. In the second step, one of the multicriteria decisionmaking methods, namely TOPSIS (Technique for Order Performance by Similarity to Ideal Solution), is used for the final ranking. Finally, after evaluating and ranking the studied patterns, the blasting pattern No. 27 is selected. This pattern is used with the properties including a hole diameter of 16.5 cm, number of holes of 13, spacing of 4 m, burden of 3 m, and ammonium nitrate fuel oil of 1100 Kg as the most appropriate blasting pattern leading to the minimum ground vibration and reduction of damages to the environment and structures constructed around the mine.
0

881
897


D.
Mohammadi
Department of Mining, Ahar Branch, Islamic Azad University Ahar, Ahar, Iran
Iran
mohammadi51.92@gmail.com


R.
Mikaeil
Department of Mining engineering, Urmia University of Technology, Urmia, Iran
Iran
reza.mikaeil@gmail.com


J.
Abdollahei Sharif
Department of Mining engineering, Urmia University, Urmia, Iran
Iran
j.a.sharif@urmia.ac.ir
Blasting
Ground Vibration
Clustering
MCDM
TOPSIS
[[1]. Asghari O, Hezarkhani A (2008) Applying discriminant analysis to separate the alteration zones within the Sungun porphyry copper deposit. J Appl Sci 8(24):4472–4486.##[2]. Armaghani, D.J., Momeni, E., Abad, S.V.A.N.K. and Khandelwal, M. (2015). Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environmental earth sciences, 74(4), 28452860.##[3]. Armaghani, D.J., Hasanipanah, M., Amnieh, H.B. and Mohamad, E.T. (2018). Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Computing and Applications, 29(9): 457465.##[4]. Aryafar, A., Mikaeil, R., Haghshenas, S.S. and Haghshenas, S.S. (2018). Application of metaheuristic algorithms to optimal clustering of sawing machine vibration. Measurement, 124: 2031.##[5]. AtashpazGargari, E. and Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation (pp. 46614667). IEEE.##[6]. Arthur, C.K., Temeng, V.A. and Ziggah, Y.Y. (2019). Novel approach to predicting blastinduced ground vibration using Gaussian process regression. Engineering with Computers, 114. doi: 10.1007/s0036601806863.##[7]. Ataei, M. and Baydokhti, H.A. (2019). An experimental study of the repeated blasting effect on surrounding rock weakness incorporating ultrasonic wave velocity measurement. Rudarskogeološkonaftni zbornik, 34 (4).##[8]. Azimi, Y., Khoshrou, S.H. and Osanloo, M. (2019). Prediction of blast induced ground vibration (BIGV) of quarry mining using hybrid genetic algorithm optimized artificial neural network. Measurement, 106874. doi:10.1016/j.measurement.2019.106874.##[9]. Dehghani, H. and Shafaghi, M. (2017). Prediction of blastinduced flyrock using differential evolution algorithm. Engineering with Computers 33 (1): 149158.##[10]. Dormishi, A.R., Ataei, M., Khaloo Kakaie, R., Mikaeil, R. and Shaffiee Haghshenas, S. (2019). Performance evaluation of gang saw using hybrid ANFISDE and hybrid ANFISPSO algorithms. Journal of Mining and Environment. 10 (2): 543557.##[11]. Faradonbeh, R.S. and Monjezi, M. (2017). Prediction and minimization of blastinduced ground vibration using two robust metaheuristic algorithms. Engineering with Computers. 33 (4): 835851. doi: 10.1007/s0036601705016.##[12]. Faradonbeh, R.S., Haghshenas, S.S., Taheri, A. and Mikaeil, R. (2019). Application of selforganizing map and fuzzy cmean techniques for rockburst clustering in deep underground projects. Neural Computing and Applications, 115. doi:10.1007/s0052101904353z##[13]. Fouladgar, N., Hasanipanah, M. and Amnieh, H.B. (2017). Application of cuckoo search algorithm to estimate peak particle velocity in mine blasting. Engineering with Computers. 33 (2): 181189.##[14]. Hasanipanah, M., Monjezi, M., Shahnazar, A., Armaghani, D.J. and Farazmand, A. (2015). Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement, 75, 289297.##[15]. Hasanipanah, M., Naderi, R., Kashir, J., Noorani, S.A. and Qaleh, A.Z.A. (2017). Prediction of blastproduced ground vibration using particle swarm optimization. Engineering with Computers. 33 (2): 173179.##[16]. Haghshenas, S.S., Haghshenas, S.S., Mikaeil, R., Sirati Moghadam, P. and Haghshenas, A.S. (2017a). A new model for evaluating the geological risk based on geomechanical properties—case study: the second part of emamzade hashem tunnel. Electron J Geotech Eng. 22 (01): 309320.##[17]. Haghshenas, S.S., Faradonbeh, R.S., Mikaeil, R., Haghshenas, S.S., Taheri, A., Saghatforoush, A. and Dormishi, A. (2019). A new conventional criterion for the performance evaluation of gang saw machines. Measurement, 146, 159170. doi: 10.1016/j.measurement.2019.06.031.##[18]. Hezarkhani A (2006) Petrology of the intrusive rocks within the Sungun Porphyry Copper Deposit Azerbaijan, Iran. J Asian Earth Sci 27: 326–340.##[19]. Hosseini, S.A., Asghari, O. Simulation of geometallurgical variables through stepwise conditional transformation in Sungun copper deposit, Iran. Arab J Geosci 8, 3821–3831 (2015). doi: 10.1007/s1251701414525.##[20]. Hosseini, S.M., Ataei, M., Khalokakaei, R., Mikaeil, R. and Haghshenas, S.S. (2019a). Study of the effect of the cooling and lubricant fluid on the cutting performance of dimension stone through artificial intelligence models. Engineering Science and Technology, an International Journal. doi: 10.1016/j.jestch.2019.04.012.##[21]. Hwang, C.L. and Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making. Springer, Berlin, Heidelberg. 186: 58191. Print ISBN 9783540105589. doi: 10.1007/9783642483189_3.##[22]. Lloyd, S.P. (1982). Least squares quantization in PCM. IEEE transactions on information theory, 28(2), 129137.##[23]. Monjezi, M., Hasanipanah, M. and Khandelwal, M. (2013). Evaluation and prediction of blastinduced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Computing and Applications, 22(78), 16371643.##[24]. Monjezi, M., Baghestani, M., Faradonbeh, R.S., Saghand, M.P. and Armaghani, D.J. (2016). Modification and prediction of blastinduced ground vibrations based on both empirical and computational techniques. Engineering with Computers. 32 (4): 717728.##[25]. Mohammadi, J., Ataei, M., Kakaie, R., Mikaeil, R. and Haghshenas, S.S. (2019). Performance evaluation of chain saw machines for dimensional stones using feasibility of neural network models. Journal of Mining and Environment, 10(4), 11051119.##[26]. Mikaeil, R., Haghshenas, S.S. and Hoseinie, S.H. (2018). Rock penetrability classification using artificial bee colony (ABC) algorithm and selforganizing map. Geotechnical and Geological Engineering. 36 (2): 13091318.##[27]. Mikaeil, R., Bakhshinezhad, H., Haghshenas, S.S. and Ataei, M. (2019). Stability Analysis of Tunnel Support Systems Using Numerical and Intelligent Simulations (Case Study: Kouhin Tunnel of QazvinRasht Railway). Rudarskogeološkonaftni zbornik. 34 (2): 110. doi: org/10.17794/rgn.2019.2.1.##[28]. National Iranian Copper Industries Company (NICICo) – Sungun Copper Project (SCP)., S.R.K Consulting, 2004. Structural Geology Model for the Sungun Copper Deposit – NW Iran.##[29]. Nguyen, H. (2019). Support vector regression approach with different kernel functions for predicting blastinduced ground vibration: a case study in an openpit coal mine of Vietnam. SN Applied Sciences. 1 (4): 283.##[30]. Nguyen, H. and Bui, X.N. (2019). Predicting blastinduced air overpressure: a robust artificial intelligence system based on artificial neural networks and random forest. Natural Resources Research. 28 (3): 893907.##[31]. Noori, A.M., Mikaeil, R., Mokhtarian, M., Haghshenas, S.S. and Foroughi, M. (2020). Feasibility of Intelligent Models for Prediction of Utilization Factor of TBM. Geotechnical and Geological Engineering, 119. doi: 10.1007/s10706020012139.##[32]. Norouzi Masir, R., Ataei, M. and Mottahedi, A. (2020). Risk assessment of flyrock in surface mines using FFTAMCDMs combination. Journal of Mining and Environment.##[33]. Shahnazar, A., Rad, H.N., Hasanipanah, M., Tahir, M.M., Armaghani, D.J. and Ghoroqi, M. (2017). A new developed approach for the prediction of ground vibration using a hybrid PSOoptimized ANFISbased model. Environmental earth sciences. 76 (15): 527.##[34]. Saghatforoush, A., Monjezi, M., Faradonbeh, R.S. and Armaghani, D.J. (2016). Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and backbreak induced by blasting. Engineering with Computers. 32 (2): 255266.##[35]. Salemi, A., Mikaeil, R. and Haghshenas, S.S. (2018). Integration of finite difference method and genetic algorithm to seismic analysis of circular shallow tunnels (Case study: Tabriz urban railway tunnels). KSCE Journal of Civil Engineering. 22 (5): 19781990.##[36]. Shang, Y., Nguyen, H., Bui, X.N., Tran, Q.H. and Moayedi, H. (2019). A Novel Artificial Intelligence Approach to Predict BlastInduced Ground Vibration in OpenPit Mines Based on the Firefly Algorithm and Artificial Neural Network. Natural Resources Research, 115.##[37]. Singh, T.N. and Singh, V. (2005). An intelligent approach to prediction and control ground vibration in mines. Geotechnical & Geological Engineering. 23 (3): 249262.##[38]. Yari, M., Bagherpour, R. and Jamali, S. (2017). Development of an evaluation system for blasting patterns to provide efficient production. Journal of Intelligent Manufacturing. 28 (4): 975984.##[39]. Zhang, X., Nguyen, H., Bui, X.N., Tran, Q. H., Nguyen, D.A., Bui, D.T. and Moayedi, H. (2019). Novel Soft Computing Model for Predicting BlastInduced Ground Vibration in OpenPit Mines Based on Particle Swarm Optimization and XGBoost. Natural Resources Research, 111.##]
1

A New Method for Predicting Indirect Tensile Strength of Sandstone Rock Samples
http://jme.shahroodut.ac.ir/article_1834.html
10.22044/jme.2020.9775.1897
1
The tensile strength (σt) of a rock plays an important role in the reliable construction of several civil structures such as dam foundations and types of tunnels and excavations. Determination of σt in the laboratory can be expensive, difficult, and timeconsuming for certain projects. Due to the difficulties associated with the experimental procedure, it is usually preferred that the σt is evaluated in an indirect way. For these reasons, in this work, the adaptive networkbased fuzzy inference system (ANFIS) is used to build a prediction model for the indirect prediction of σt of sandstone rock samples from their physical properties. Two ANFIS models are implemented, i.e. ANFISsubtractive clustering method (SCM) and ANFISfuzzy cmeans clustering method (FCM). The ANFIS models are applied to the data available in the open source literature. In these models, the porosity, specific gravity, dry unit weight, and saturated unit weight are utilized as the input parameters, while the measured σt is the output parameter. The performance of the proposed predictive models is examined according to two performance indices, i.e. mean square error (MSE) and coefficient of determination (R2). The results obtained from this work indicate that ANFISSCM is a reliable method to predict σt with a high degree of accuracy.
0

899
908


H.
Fattahi
Department of Earth Sciences Engineering, Arak University of Technology, Arak, Iran
Iran
h.fattahi@arakut.ac.ir
tensile strength
Physical Properties
ANFIS
Subtractive clustering method
Fuzzy Cmeans clustering method
[[1]. Baykasoğlu, A., Güllü, H., Çanakçı, H. and Özbakır, L. (2008). Prediction of compressive and tensile strength of limestone via genetic programming. Expert Syst Appl 35 (1):111123.##[2]. Ceryan, N., Okkan, U. and Kesimal, A. (2012). Application of generalized regression neural networks in predicting the unconfined compressive strength of carbonate rocks. Rock Mech Rock Eng 45 (6):10551072.##[3]. Kahraman, S. and Yeken, T. (2010). Electrical resistivity measurement to predict uniaxial compressive and tensile strength of igneous rocks. B Mater Sci 33 (6):731735.##[4]. Singh, V. and Singh, D. and Singh, T. (2001). Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks. Int J Rock Mech Min Sci 38 (2):269284.##[5]. Cai, M. (2010). Practical estimates of tensile strength and Hoek–Brown strength parameter m i of brittle rocks. Rock Mech Rock Eng 43 (2):167184.##[6]. Karakus, M. (2011). Function identification for the intrinsic strength and elastic properties of granitic rocks via genetic programming (GP). Comput Geosci 37 (9):13181323.##[7]. Chen, G., Jia, Z. and Ke, J. (1997). Probabilistic analysis of underground excavation stability. Int J Rock Mech Min Sci 34 (34):51. e5151. e16.##[8]. Heidari, M., Khanlari, G., Torabi Kaveh, M. and Kargarian, S. (2012). Predicting the uniaxial compressive and tensile strengths of gypsum rock by point load testing. Rock Mech Rock Eng 45 (2):265273.##[9]. Abolhosseini, H., Hashemi, M. and Ajalloeian, R. (2020). Evaluation of geotechnical parameters affecting the penetration rate of TBM using neural network (case study). Arab J Geosci 13 (4):183.##[10]. Iphar, M., Yavuz, M. and Ak, H. (2008). Prediction of ground vibrations resulting from the blasting operations in an openpit mine by adaptive neurofuzzy inference system. Environ Geol 56 (1):97107.##[11]. Sezer, E.A., Nefeslioglu, H.A. and Gokceoglu, C. (2014). An assessment on producing synthetic samples by fuzzy Cmeans for limited number of data in prediction models. Appl Soft Comput 24:126134.##[12]. Fattahi, H. (2016). Adaptive neuro fuzzy inference system based on fuzzy C–means clustering algorithm, a technique for estimation of TBM peneteration rate. Int J Optim Civil Eng 6 (2):159171.##[13]. Fattahi, H .(2016). Indirect estimation of deformation modulus of an in situ rock mass: an ANFIS model based on grid partitioning, fuzzy cmeans clustering and subtractive clustering. J Geosci 20 (5):681–690.##[14]. Karimpouli, S. and Fattahi, H. (2018), Estimation of Pand Swave impedances using Bayesian inversion and adaptive neurofuzzy inference system from a carbonate reservoir in Iran. Neural Comput Appl 29 (11):10591072.##[15]. Fattahi, H. and Karimpouli, S. (2016). Prediction of porosity and water saturation using prestack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods. Computat Geosci 20 (5):10751094.##[16]. Jang, JS .(1993). ANFIS: adaptivenetworkbased fuzzy inference system. IEEE T Syst Man Cyb 23 (3):665685.##[17]. Weiling C, Lee J Fuzzy Logic for the Applications to Complex Systems. In: Proceedings of the International Joint Conference of CFSA/IFIS/SOFT on Fuzzy Theory and Applications. Singapore et al.: World Scientific, 1995.##[18]. Chiu, S.L. (1994). Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2 (3):267278##[19]. Bezdek, J.C. (1973). Fuzzy mathematics in pattern classification. Cornell university, Ithaca.##[20]. Ghobadi, M.H., Mousavi, S., Heidari, M. and Rafie, B. (2015). The Prediction of the Tensile Strength of Sandstones from their petrographical properties using regression analysis and artificial neural network. Geopersia 5 (2):177187.##[21]. Gholami, R., Moradzadeh, A., Maleki, S., Amiri, S. and Hanachi, J. (2014). Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs. J Pet Sci Eng 122:643656.##[22]. Jayalakshmi, T. and Santhakumaran, A. (2011). Statistical normalization and back propagation for classification. International Journal of Computer Theory and Engineering 3 (1):17938201.##[23]. Fattahi, H. (2016). Application of improved support vector regression model for prediction of deformation modulus of a rock mass. Eng Comput 32 (4):567580.##[24]. Fattahi, H. (2017). Applying soft computing methods to predict the uniaxial compressive strength of rocks from schmidt hammer rebound values. Computat Geosci 21 (4):665681. doi:10.1007/s1059601796423.##[25]. Fattahi, H. and Bazdar, H. (2017). Applying improved artificial neural network models to evaluate drilling rate index. Tunn Undergr Sp Tech 70:114124.##[26]. Karimpouli, S. and Fattahi, H .(2018). Estimation of Pand Swave impedances using Bayesian inversion and adaptive neurofuzzy inference system from a carbonate reservoir in Iran. Neural Comput Appl 29 (11):1059–1072.##]
1

Development of a New Experimental Technique for Dynamic Fracture Toughness Measurement of Rocks using Drop Weight Test
http://jme.shahroodut.ac.ir/article_1840.html
10.22044/jme.2020.9818.1903
1
The dynamic fracture characteristics of rock specimens play an important role in analyzing the fracture issues such as blasting, hydraulic fracturing, and design of supports. Several experimental methods have been developed for determining the dynamic fracture properties of the rock samples. However, many used setups have been manufactured for metal specimens, and are not suitable and efficient for rocks. In this work, a new technique is developed to measure the dynamic fracture toughness of rock samples and fracture energy by modifying the drop weight test machine. The idea of wave transmission bar from the Hopkinson pressure bar test is applied to drop weight test. The intact samples of limestone are tested using the modified machine, and the results obtained are analyzed. The results indicate that the dynamic fracture toughness and dynamic fracture energy have a direct linear relationship with the loading rate. The dynamic fracture toughness and dynamic fracture energy of limestone core specimens under the loading rates of 0.120.56kN/µS are measured between 9.618.51MPa√m and 1249.734646.08J/m2, respectively. In order to verify the experimental results, a series of numerical simulation are conducted in the ABAQUS software. Comparison of the results show a good agreement where the difference between the numerical and experimental outputs is less than 4%. It can be concluded that the new technique on modifying the drop weight test can be applicable for measurement of the dynamic behavior of rock samples. However, more tests on different rock types are recommended for confirmation of the application of the developed technique for a wider range of rocks.
0

909
920


Gh.
Khandouzi
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
Iran
gh.khandouzi@ut.ac.ir


H.
Memarian
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
Iran
memarian@ut.ac.ir


M. H.
Khosravi
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
Iran
mh.khosravi@ut.ac.ir
Dynamic fracture toughness
Drop weight test
Numerical Simulation
[[1]. Khandouzi, G.H., Mollashahi, M. and Moosakhani, M. (2019). Numerical simulation of crack propagation behavior of a semicylindrical specimen under dynamic loading. Frattura ed Integrità Strutturale. 50:2937; DOI: 10.3221/IGFESIS.50.04.##[2]. Saghafi, H.A., Ayatollahi, M.R. and Sistani, M. (2010). A modified MTS criterion (MMTS) for mixed mode fracture toughness assessment of brittle materials. Material science and engineering: A. 527:562430.##[3]. Chen, C.S. Pan, E. and Amadei, B. (1998). Fracture mechanics analysis of cracked discs of anisotropic rock using the boundary element method. International journal of rock mechanics & mining sciences. 35:195218.##[4]. Khandouzi, G.H., Mirmohhamadlou, A. and Memarian, H. (2014). Dynamic fracture behavior of cubic and core specimen under impact load. Rock engineering and rock mechanic. 14954. DOI: 10.1201/b1695522.##[5]. Franklin, J.A. and Atkinson, B.K. (1988). Suggested methods for determining the fracture toughness of rock. Int J Rock Mech Min Sci goemechanics Abstract. 25 (2):71–96.##[6]. Fowell, R.J., Xu, C. and Chen, J.F. (1995). Suggested method for determining modeI fracture toughness using cracked chevronnotched Brazilian disc (CCNBD) specimens. Int J Rock Mech Min Sci goemechanics Abstract. 32 (1):57–64.##[7]. Chunan. T. and Xiaohe, X. (1990). A new method for measuring dynamic fracture toughness of rock, engineering fracture mechanics. International journal of fracture Mechanics. Vol. 35, NO. 4/S, pp. 783791.##[8]. Wang, Q.Z., Feng, F., Ni, M. and Gou, X.P. (2011). Measurement of mode I and mode II rock dynamic fracture toughness with cracked straight through flattened Brazilian disc impacted by split Hopkinson pressure bar. Engineering Fracture Mechanics. 78:2455–69.##[9]. Wang. Q.Z., Zhang, S. and Xie, H.P. (2009). Rock Dynamic Fracture Toughness Tested with Holedcracked Flattened Brazilian Discs. Proceedings of the International Congress and Exposition, Orlando, Florida USA. 50:87785.##[10]. Nikita, F. Morozov., Yuri, V. petrov., Vladimir, I. Smirnov. (2009). Dynamic Fracture of Rocks. 7th EUROMECH Solid Mechanics Conference. Lisbon, Portugal. September 7th11th.##[11]. Chen, R. Xia, K., Dai, F., Lu, F. and Luo, S.N. (2009). Determination of dynamic fracture parameters using a semicircular bend technique in split Hopkinson pressure bar testing. Engineering Fracture Mechanics. 76:1268–76.##[12]. Dai. F., Chen, R., Iqbal, M.J. and Xia, K. (2010). Dynamic cracked chevron notched Brazilian disc method for measuring rock fracture parameters. International Journal of Rock Mechanics & Mining Sciences. 47: 606–13.##[13]. Yao. W. and Xia, K. (2019). Dynamic notched semicircle bend (NSCB) method for measuring fracture properties of rocks: Fundamentals and applications. Journal of rock mechanics and geotechnical engineering. 11: 10661093.##[14]. Shi. X., Yao. W., Liu. D., Xia. K., Tang. T. and Shi. Y. (2019). Experimental study of the dynamic fracture toughness of anisotropic black shale using notched semicircular bend specimens. Engineering fracture mechanics. 205: 136151.##[15]. Liu. X.R., Yang. S.Q., Huang. Y.H. and Chen. J.L. (2019). Experimental study on the strength and fracture mechanism of sandstone containing elliptical holes and fissures under uniaxial compression. Engineering fracture mechanics. 205: 205217.##[16]. Omer, Y.B., ozkan, o. and Atban, R.A. (2017). The effect of nanosilica on charpy impact behavior of glass/epoxy fibr rienfoced composite laminate. Periodical of engineering and natural science, 5: 322327.##[17]. Abrate, S. (2011). Impact engineering of composite structures. Springer Wien New York, Printed in Italy. ISBN 9783709105221.##[18]. Lorriot, T., Martin, E., Quenisset, J.M. and Rebiere, J.P. (1998). Dynamic analysis of instrumented CHARPY impact tests using specimen deflection measurement and massspring models. International Journal of Fracture. (91):299309.##[19]. Jiang, F. and Vecchio, K.S. (2009). Hopkinson Bar Loaded Fracture Experimental Technique: A Critical Review of Dynamic Fracture Toughness Tests. Applied Mechanics. DOI: 10.1115/1.3124647.##[20]. Chunhuan, G., Fengchun, J., Ruitang, L. and Yang Y. (2011). Size effect on the contact state between fracture specimen and supports in Hopkinson bar loaded fracture test. Int JFract.169:77–84.##[21]. Sheikh, A. K., Arif, A.F.M. and Qamar. S.Z. (2002). Determination of fracture toughness of tool steels. The 6th Saudi Engineering Conference, KFUPM, Dhahran. 5:169.##[22]. Zhang, B.Q. and Zhao, J. (2014). A review of dynamic experimental techniques and mechanical behavior of rock materials. Rock mechanic and rock engineering. (47):141178.##[23]. Manhan, M.P. and Stonesifer, R.B. (2007). Studied toward optimum instrumented striker designs. European structure integrity society. (30):2218.##[24]. Knapp, J., Altmann, E., Niemann, J. and Warner, K.D. (1998). Measurement of shock events by means of strain gauges and accelerometers. Measurement Elsevier. (24):8796.##[25]. Lou, J., Ying, K., He, P. and Bai, J. (2005). Properties of Savitzky–Golay digital differentiators. Digital Signal Processing. (18):12236.##[26]. Ouchterlony, F. (1981). Extension of compliance and stress intensity formulas for the single edge cracked round bar in bending. ASTM STP 678. 166182.##[27]. Saouma, V.E. (2000). Lecture Notes in fracture mechanics. CVEN.6831, University of Colorado, Boulder. CO:803090428, 2000.##]
1

Investigation of Mechanism of Adsorption of Xanthate and Hydroxamate on Malachite
http://jme.shahroodut.ac.ir/article_1846.html
10.22044/jme.2020.9755.1895
1
Copper oxide minerals such as malachite do not respond well to the traditional copper sulfide collectors, and require alternative flotation schemes. In many copper ore mines, significant copper oxide minerals, especially malachite, are associated with sulfide minerals. Considering that xanthates are most widely used in the flotation of sulfide minerals as well as copper sulfide minerals and, hydroxamate has shown a good selectivity for copper oxide minerals. Use of the synergistic effect of xanthate and hydroxamate can be an effective way to increase the flotation efficiency of copper oxide minerals along with sulfide minerals. In this work, we investigate the individual interactions of potassium amyl xanthate (PAX) and potassium alkyl hydroxamate (HXM) with the natural malachite and explore their synergistic effects on the malachite flotation. The results of solubility of malachite in collector solutions, changes in the malachite surface potential, adsorption kinetics, adsorption densities, dynamic contact angles, FTIR analyses, and smallscale flotations, are discussed. The results obtained demonstrate that PAX and HXM are chemically coadsorbed on the malachite surface, and the amount of PAX adsorbed on the malachite surface is considerably increased in the mixed PAX/HXM systems because of the coadsorption mechanism. The flotation results confirm that the mixed PAX/HXM exhibit a superior flotation performance of malachite compared to the individual system of PAX or HXM. Based on these results, the mixed PAX/HXM exhibit a remarkable synergism effect on malachite surface hydrophobicity.
0

921
933


M.
Mohammadkhani
Department of mining Engineering, Tarbiat Modares University, Tehran, Iran
Iran
m.mkhani@yahoo.com


M.
Abdollahy
Department of mining Engineering, Tarbiat Modares University, Tehran, Iran
Iran
minmabd@modares.ac.ir


M. R.
Khalesi
Department of mining Engineering, Tarbiat Modares University, Tehran, Iran
Iran
mrkhalesi@modares.ac.ir
Malachite
surface potential
Adsorption
Synergism
Froth Flotation
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Fuerstenau, D.W., HerreraUrbina, R. and McGlashan, D.W. (2000). Studies on the applicability of chelating agents as universal collectors for copper minerals. International Journal of Mineral Processing, 58(14), 1533.##[8]. Srdjan, M.B. (2010). Handbook of Flotation Reagents: Chemistry, Theory and Practice: Volume 2: Flotation of Gold, FGM and Oxide Minerals.##[9]. Jiayang, H., Jianlong, D., Wentao, L., Bin, Y. and Beijun, L. (2013). Molecular modeling of alkyl hydroxamates as a highly selective flotation collectors for oxidized copper mineral. 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA) (pp. 353356). IEEE.##[10]. Rybinski, V,W. Schwuger, M.J. and Dobias, B. (1987). Surfactant mixtures as collectors in flotation. Colloids and surfaces, 26, 291304.##[11]. Chowdhury, R. and Antolasic, F. (2012). Structural analysis of hydroxamate reagents by Xray diffraction. Journal of Earth Science and Engineering. 2 (10): 584.##[12]. Hope, G.A., Woods, R., Parker, G.K., Buckley, A.N. and McLean, J. (2010). A vibrational spectroscopy and XPS investigation of the interaction of hydroxamate reagents on copper oxide minerals. Minerals Engineering. 23 (1113): 952959.##[13]. Li, Z. (2019). Effect of salinity and overgrinding on the flotation of malachite.##[14]. Buckley, A.N., Denman, J.A. and Hope, G.A. (2012). The adsorption of noctanohydroxamate collector on Cu and Fe oxide minerals investigated by static secondary ion mass spectrometry. Minerals. 2 (4): 493515.##[15]. Hope, G.A., Buckley, A.N., Parker, G.K., Numprasanthai, A., Woods, R. and McLean, J. (2012). The interaction of noctanohydroxamate with chrysocolla and oxide copper surfaces. Minerals Engineering, 36, 211.##[16]. Mendiratta, N.K. (2000). Kinetic studies of sulfide mineral oxidation and xanthate adsorption (Doctoral dissertation, Virginia Tech).##[17]. Zhang, Y., Cao, Z., Cao, Y. and Sun, C. (2013). FTIR studies of xanthate adsorption on chalcopyrite, pentlandite and pyrite surfaces. Journal of Molecular Structure, 1048, 434440.##[18]. Mielczarski, J. and Leppinen, J. (1987). Infrared reflectionabsorption spectroscopic study of adsorption of xanthates on copper. Surface Science. 187 (23): 526538.##[19]. Mielczarski, J. and Leppinen, J. (1987). Infrared reflectionabsorption spectroscopic study of adsorption of xanthates on copper. Surface Science. 187 (23): 526538.##[20]. Rao, S.R. and Finch, J.A. (2003). Base metal oxide flotation using long chain xanthates. International Journal of Mineral Processing. 69 (14): 251258.##[21]. Davidson, M.S. (2009). An investigation of copper recovery from a sulphide oxide ore with a mixed collector system. In Masters Abstracts International (Vol. 49, No. 02).##[22]. Heyes, G.W., Allan, G.C., Bruckard, W.J. and Sparrow, G.J. (2012). Review of flotation of feldspar. Mineral Processing and Extractive Metallurgy. 121 (2): 7278.##[23]. Cui, X., Jiang, Y., Yang, C., Lu, X., Chen, H., Mao, S. and Du, Y. (2010). Mechanism of the mixed surfactant micelle formation. The Journal of Physical Chemistry B. 114 (23): 78087816.##[24]. Buckley, A.N., Hope, G.A., Parker, G.K., Steyn, J. and Woods, R. (2017). Mechanism of mixed dithiophosphate and mercaptobenzothiazole collectors for Cu sulfide ore minerals. Minerals Engineering. 109: 8097.##[25]. Xu, L., Tian, J., Wu, H., Lu, Z., Sun, W. and Hu, Y. (2017). The flotation and adsorption of mixed collectors on oxide and silicate minerals. Advances in colloid and interface science. 250: 114.##[26]. Lotter, N.O. and Bradshaw, D.J. (2010). The formulation and use of mixed collectors in sulphide flotation. Minerals engineering, 23(1113), 945951.##[27]. Xu, L., Hu, Y., Tian, J., Wu, H., Wang, L., Yang, Y. and Wang, Z. (2016). Synergistic effect of mixed cationic/anionic collectors on flotation and adsorption of muscovite. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 492, 181189.##[28]. Li, F., Zhong, H., Xu, H., Jia, H. and Liu, G. (2015). Flotation behavior and adsorption mechanism of αhydroxyoctyl phosphinic acid to malachite. Minerals engineering. 71: 188193.##[29]. Liu, G., Huang, Y., Qu, X., Xiao, J., Yang, X. and Xu, Z. (2016). Understanding the hydrophobic mechanism of 3hexyl4amino1, 2, 4triazole5thione to malachite by ToFSIMS, XPS, FTIR, contact angle, zeta potential and microflotation. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 503, 3442.##[30]. Yin, W.Z., Sun, Q.Y., Dong, L.I., Yuan, T.A.N.G., Fu, Y.F. and Jin, Y.A.O. (2019). Mechanism and application on sulphidizing flotation of copper oxide with combined collectors. Transactions of Nonferrous Metals Society of China. 29 (1): 178185.##[31]. Liu, J., Hu, Z., Liu, G., Huang, Y. and Zhang, Z. (2020). 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