ORIGINAL_ARTICLE
Investigation of the effect of industrial ball mill liner type on their comminution mechanism using DEM
The mill shell liner type, rotation speed and the amount of its loading are the key factors influencing the charge behavior, consequently the comminution mechanism. In this paper, milling operation of industrial ball mills using Discrete Element Method (DEM) is investigated. First, an industrial scale ball mill with a Smooth liner type is simulated. Then, by changing liner type, i.e. Wave, Rib, Ship-lap, Lorain, Osborn, and Step liners, six other independent simulations are performed. Effects of mill shell liner type on charge shoulder, toe, impact, and head points, also on 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 simulation results, a laboratory scale mill is simulated. The results indicate that the charge heads are respectively about 240.13, 283.40, 306.47, 278.12, 274.42, 274.42, and 278.12 cm at the simulations performed with Smooth, Wave, Rib, Ship-lap, Lorain, Osborn, and Step liners at 70% of NC. The corresponding values at 80% of NC are as follows: 256.08, 264.56, 313.54, 298.45, 313.54, 311.60, and 283.40 cm. On the other hand, the impact zone lengths are respectively about 33.14, 22.11, 38.63, 35.86, 38.63, 38.63, and 49.59 cm at the simulations performed with above-mentioned liners at 70% of NC. The corresponding values for impact zone lengths at 80% of NC are as follows: 35.86, 27.63, 49.59, 38.63, 33.14, 52.32, and 41.38 cm. Comparison of the simulations related to the laboratory scale mill with experimental results demonstrates a good agreement which validates the DEM simulations and the software used.
https://ijmge.ut.ac.ir/article_82043_d76b3492099e0dcd559316c55414be61.pdf
2021-12-01
97
107
10.22059/ijmge.2020.289423.594826
DEM simulation
Industrial ball mills
Liner type
Head height
Impact zone length
Sajad
Kolahi
sajadkolahi74@gmail.com
1
Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
AUTHOR
Mohammad
Jahani Chegeni
m.jahani1983@gmail.com
2
Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
LEAD_AUTHOR
Kumars
Seifpanahi- Shabani
q.s11063@yahoo.com
3
Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
AUTHOR
[1] G. Rosales-Marín, J. Andrade, G. Alvarado, J.A. Delgadillo, E.T. Tuzcu, “Study of lifter wear and breakage rates for different lifter geometries in tumbling mill: Experimental and simulation analysis using population balance model”, Minerals Engineering, 141, September 2019.
1
[2] B. K. Mishra and R. K. Rajamani, “The discrete element method for the simulation of ball mills”, Appl. Math. Modelling, 16, 598 – 604, 1992.
2
[3] M.S. Powell, A.T. McBride, “A three-dimensional analysis of media motion and grinding regions in mills”, Minerals Engineering, 17, 1099–1109, 2004.
3
[4] P.W. Cleary, “Predicting charge motion, power draw, segregation and wear in ball mills using discrete element methods”, Minerals Engineering, 11, 1061-1080, 1998.
4
[5] P. W. Cleary, “Charge behaviour and power consumption in ball mills: sensitivity to mill operating conditions, liner geometry and charge composition”, Int. J. Miner. Process. 63, 79–114, 2001.
5
[6] J. T. Kalala, M. M. Bwalya, M. H. Moys, “Discrete element method (DEM) modelling of evolving mill liner profiles due to wear. Part I: DEM validation”, Minerals Engineering 18,1386–1391, 2005.
6
[7] J. T. Kalala, M. Bwalya, M.H. Moys, “Discrete element method (DEM) modelling of evolving mill liner profiles due to wear. Part II. Industrial case study”, Minerals Engineering, 18, 1392–1397, 2005.
7
[8] S. Banisi, M. Hadizadeh, “3-D liner wear profile measurement and analysis in industrial SAG mills”, Minerals Engineering, 20, 132– 139, 2007.
8
[9] M. Yahyaei, S. Banisi, M. Hadizadeh, “Modification of SAG mill liner shape based on 3-D liner wear profile measurements”, Int. J. Miner. Process. 91, 111–115, 2009.
9
[10] P. W. Cleary, “Recent Advances in DEM modelling of tumbling mills”, Minerals Engineering, 14, 1295 – 1319, 2001. [11] M. K. Abd El-Rahman, B. K. Mishra, R. K. Rajamani, “Industrial tumbling mill power prediction using the discrete element method”, Minerals Engineering, 14, 1321–1328, 2001.
10
[11] M. K. Abd El-Rahman, B. K. Mishra, R. K. Rajamani, “Industrial tumbling mill power prediction using the discrete element method”, Minerals Engineering, 14, 1321–1328, 2001.
11
[12] P. W. Cleary, R. Morrisson, S. Morrell, “Comparison of DEM and experiment for a scale model SAG mill”, Int. J. Miner. Process., 68,129– 165, 2003.
12
[13] B. K. Mishra, “A review of computer simulation of tumbling mills by the discrete element method Part II—Practical applications”, Int. J. Miner. Process., 71, 95– 112, 2003.
13
[14] R. D. Morrison, P. W. Cleary, “Using DEM to model ore breakage within a pilot-scale SAG mill”, Minerals Engineering 17, 1117– 1124, 2004.
14
[15] N. Djordjevic, F. N. Shi, R. Morrison, “Determination of lifter design, speed and filling effects in AG mills by 3D DEM”, Minerals Engineering, 17, 1135–1142, 2004.
15
[16] N. Djordjevic, R. Morrison, B. Loveday, P. Cleary, “Modelling comminution patterns within a pilot-scale AG/SAG mill”, Minerals Engineering, 19, 1505–1516, 2006.
16
[17] M. Maleki-Moghaddam, A. R. Ghasemi, M. Yahyaei, S. Banisi, “The impact of the end-wall effect on the charge trajectory in tumbling model mills”, Int. J. Miner. Process., 144, 75 – 80, 2015.
17
[18] P. Owen, P. W. Cleary, “The relationship between charge shape characteristics and fill level and lifter height for a SAG mill”, Minerals Engineering, 83, 19–32, 2015.
18
[19] N. S. Weerasekara, L. X. Liu, M. S. Powell, “Estimating energy in grinding using DEM modelling”, Minerals Engineering, 85, 23– 33, 2016.
19
[20] P. W. Cleary, P. Owen, “Development of models relating charge shape and power draw to SAG mill operating parameters and their use in devising mill operating strategies to account for liner wear”, Minerals Engineering, 117, 42–62, 2018.
20
[21] L. Xu, K. Luo, Y. Zhao, “Numerical prediction of wear in SAG mills based on DEM simulations”, Powder Technology, 329, 353–363, 2018.
21
[22] P. W. Cleary, P. Owen, “Effect of operating condition changes on the collisional environment in a SAG mill”, Minerals Engineering, 132, 297–315, 2019.
22
[23] A. R. Hasankhoei, M. Maleki-Moghaddam, A. Haji-Zadeh, M. E. Barzgar, S. Banisi, “On dry SAG mills end liners: Physical modeling, DEM-based characterization and industrial outcomes of a new design”, Minerals Engineering, 141, 105835, 2019.
23
[24] X. Bian, G. Wang, H. Wang, S. Wang, W. Lv, “Effect of lifters and mill speed on particle behaviour, torque, and power consumption of a tumbling ball mill: Experimental study and DEM simulation”, Minerals Engineering, Minerals Engineering,105, 22 – 35, 2017.
24
[25] F. Pedrayes, J. G. Norniella, M. G. Melero, J. M. Menéndez-Aguado, J. J. Juan J. del Coz-Díaz, “Frequency domain characterization of torque in tumbling ball mills using DEM modelling: Application to filling level monitoring”, Powder Technology, 323, 433 – 444, 2018.
25
[26] B. A. Wills, T.J., Napier-Munn, “Wills' Mineral Processing Technology”, 8th edition. Elsevier, pp. 147–180. Chapter 7, 2016. [27] P. W. Cleary, “Large scale industrial DEM modeling”, Eng. Comput. 21,169–204, 2004.
26
[28] P. W. Cleary, M. D. Sinnott, R. D. Morrison, “DEM prediction of particle flows in grinding processes”, Int. J. Numer. Methods Fluids 58, 319–353, 2008.
27
[29] P. W. Cleary, “Ball motion, axial segregation and power consumption in a full scale two chamber cement mill”, Miner. Eng. 22, 809–820, 2009.
28
[30] J. Kozicki, F. V. Donzé, “YADE-OPEN DEM: an open-source software using a discrete element method to simulate granular material”, Eng. Comput. 26, 786–805, 2009.
29
[31] J. Chen, B. Huang, F. Chen, X. Shu, “Application of discrete element method to Superpave gyratory compaction”, Road Mater. Pavement 13, 480–500, 2012.
30
[32] L. Zhang, S.F. Quigley, A. H. C. Chan, “A fast scalable implementation of the two-dimensional triangular Discrete Element Method on a GPU platform”, Adv. Eng. Softw. 60–61, 70–80, 2013.
31
[33] H. Kruggel-Emden, M. Sturm, S. Wirtz, V. Scherer, “Selection of an appropriate time integration scheme for the discrete element method (DEM)”, Comput. Chem. Eng. 32, 2263–2279, 2008.
32
[34] B. Nassauer, T. Liedke, M. Kuna, “Polyhedral particles for the discrete element method Geometry representation, contact detection, and particle generation”, Granul. Matter, 15, 85–93, 2013.
33
[35] A. O. Raji, J. F. Favier, “Model for the deformation in agricultural and food particulate materials under bulk compressive loading using discrete element method. I: theory, model development, and validation”, J. Food Eng. 64, 359–371, 2004.
34
[36] B. Nassauer, M. Kuna, “Contact forces of polyhedral particles in discrete element method”, Granul. Matter, 15, 349–355, 2013.
35
[37] R. Balevičius, A. Džiugys, R. Kačianauskas, A. Maknickas, K. Vislavičius, “Investigation of performance of programming approaches and languages used for numerical simulation of granular material by the discrete element method”, Comput. Phys. Commun. 175, 404–415, 2006.
36
[38] G. W. Delaney, P. W. Cleary, R. D. Morrison, S. Cummins, B. Loveday, “Predicting breakage and the evolution of rock size and shape distributions in Ag and SAG mills using DEM”, Miner. Eng. 50–51, 32–139, 2013.
37
[39] J. M. Ting, M. Khwaja, L. R. Meachum, J. D. Rowell, “An ellipsebased discrete element model for granular materials”, Int. J. Numer. Anal. Methods Geomech. 17, 603–623, 1993.
38
[40] I. Shmulevich, “State of the art modeling of soil–tillage interaction using discrete element method”, Soil Tillage Res. 111, 41–53, 2010.
39
[41] P. W. Cleary, M. L. Sawley, “DEM modelling of industrial granular flows: 3D case studies and the effect of particle shape on hopper discharge, Appl. Math. Model. 26, 89–111, 2002.
40
[42] P. W. Cleary, “Industrial particle flow modelling using discrete element method, Eng. Comput. 26, 698–743, 2009.
41
[43] P. W. Cleary, M. D. Sinnott, “Assessing mixing characteristics of particle-mixing and granulation devices, Particuology, 6, 419–444, 2008.
42
[44] P. W. Cleary, “DEM prediction of industrial and geophysical particle flows”, Particuology, 8, 106–118, 2010.
43
[45] P. W. Cleary, R. D. Morrison, “Particle methods for modelling in mineral processing”, Int. J. Comput. Fluid Dyn. 23,137–146, 2009.
44
[46] S. Just, G. Toschkoff, A. Funke, D. Djuric, G. Scharrer, J. Khinast, K. Knop, P. Kleinebudde, “Experimental analysis of tablet properties for discrete element modeling of an active coating process”, AAPS PharmSciTech 14, 402–411, 2013.
45
[47] W. McBride, P. W. Cleary, “An investigation and optimization of the ‘OLDS’ elevator using Discrete Element Modeling”, Powder Technol. 193, 216–234, 2009.
46
[48] C. Goniva, C. Kloss, N.G. Deen, J.A.M. Kuipers, S. Pirker, “Influence of rolling friction on single spout fluidized bed simulation Particuology 10, 582–591, 2012.
47
[49] C. Goniva, C. Kloss, A. Hager, S. Pirker, “An open-source CFD–DEM perspective”, Proceedings of OpenFOAM Workshop Gothenburg, Sweden, 2010.
48
[50] C. Kloss, C. Goniva, G. Aichinger, S. Pirker, “Comprehensive DEM– DPM–CFD simulations—model synthesis, experimental validation and scalability”, Seventh International Conference on CFD in the Minerals and Process Industries CSIRO, Melbourne, Australia, 2009.
49
[51] B. A. Wills, “Wills’ Mineral Processing Technology – An Introduction to the Practical Aspects of Ore Treatment and Mineral Recovery”, Eighth Edition, Butterworth-Heinemann, Elsevier, 2016.
50
[52] N.S. Weerasekara, M.S. Powell, P. W. Cleary, L.M. Tavares, M. Evertsson, R.D. Morrison, J. Quist, R.M. Carvalho, “The contribution of EM to the science of comminution”, Powder Technol. 248 (2013) 3–24.
51
ORIGINAL_ARTICLE
Studies on the effects of physical parameters of filtration process on the fluid flow characteristics and de-watering efficiency of copper concentrate
The effect of physical parameters such as type of filtration media, solids percent, pressure drop, and pH on resistance to filter cloth (R), specific cake resistance (α), moisture content and cake formation rate were investigated in this paper. Experiments were performed using Vacuum Top-Feed method and during the experiments, no chemicals (flocculants, coagulants, etc.) were used. The optimal response for each factor was considered as the minimum values of the resistance to filter cloth (R), specific cake resistance (α), moisture content and maximum cake formation rate. The results showed that the Cloth A7 (Fiber: Polyester, Weave: Twill) and Cloth A12 (Fiber: Polyester, Weave: Plain) have the best performance among 16 types of filters media. With increasing solid content from 45 to 65%, the resistance to filter cloths of A7 and A12 increase from 26.29 (1/m×〖10〗^10) to 101.39 (1/m×〖10〗^10) and from 25.38(1/m×〖10〗^10) to 245.67 (1/m×〖10〗^10), respectively. The highest rate of cake formation in 65% solids for both cloths was 0.077 (mm/s) for cloth A7 and 0.059 (mm/s) for cloth A12. Also, it was found that the compressibility factor is the same for each cloths, so the difference in the compressibility coefficient of cake depends on the inherent properties of the raw material.
https://ijmge.ut.ac.ir/article_82444_403be9ac44ef5624aa8ad178c2e6d25f.pdf
2021-12-01
109
116
10.22059/ijmge.2020.295976.594839
filtration
Copper concentrate
Physical parameters
Water recovery
Amirhossein
Rezaei
arash.rezaei138@gmail.com
1
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Hadi
Abdollahi
h_abdollahi@ut.ac.ir
2
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Mehdi
Gharabaghi
gharabaghi@ut.ac.ir
3
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
[1] Gálvez, E.D., Cruz, R., Robles, P.A., Cisternas. L.A. (2014). Optimization of dewatering systems for mineral processing. Minerals Engineering. 63: p.110-117.
1
[2] Klemes, J.J. (2012). Industrial water recycle/reuse. Current Opinion in Chemical Engineering. 1(3): p. 238-245.
2
[3] Usher, S.P., and Scales, P.J. (2005). Steady state thickener modelling from the compressive yield stress and hindered settling function. Chemical Engineering Journal. 111(2-3): p. 253-261.
3
[4] Bürger, R., Concha, F., Karlsen, K.H. (2001). Phenomenological model of filtration processes: 1. Cake formation and expression. Chemical Engineering Science. 56(15): p. 4537-4553.
4
[5] Chuvaree, R., Otani, Y., Mizukami, Y., Tanaka, T. (2007). Study on the fundamental characteristics of a filter press dryer with a featured model dryer. Advanced Powder Technology. 18(3): p. 273-285.
5
[6] Wills, B.A., and Napier-Munn, T.J. (2006). An introduction to the practical aspects of ore treatment and mineral recovery. Wills' Mineral Processing Technology. p. 267-352.
6
[7] Adrian, H., and Meintjes, C. (2005). Dry tailings disposal-increased production and potential water saving. Mwale, AH, Musonge, P., Fraser, DM., p. 915-926.
7
[8] Stickland, A.D., Skinner, S.J., Cavalida, R.G., Scales, P.J. (2018). Optimization of filter design and operation for wastewater treatment sludge. Separation and Purification Technology. 198: p. 31-37.
8
[9] Wang, Q., Lin, X., Chen, D.R. (2016). Effect of dust loading rate onthe loading characteristics of high-efficiency filter media. Powder Technology. 287: p.20-28.
9
[10] Luo, H., Ning, X.A., Liang, X., Feng, Y., Liu, J. (2013). Effects of sawdust-CPAM on textile dyeing sludge dewaterability and filter cake properties. Bioresource Technology. 139: p. 330-336.
10
[11] Huttunen, M., Nygren, L., Kinnarinen, T., Ekberg, B., Lindh, T., Karvonen, V., Ahola, J., Häkkinen, A. (2019). Real-time monitoring of the moisture content of filter cakes in vacuum filters by a novel soft sensor. Separation and Purification Technology, 223: p. 282-291.
11
[12] Sparks, T. (2012). Solid-liquid filtration: Understanding filter presses and belt filters. Filtration+ Separation. 49(4): p. 20-24.
12
[13] Tarleton, S., and Wakeman, R. (2006). Solid/liquid separation: equipment selection and process design. Elsevier.
13
[14] Gadhave, A.D., Mehdizadeh, S.N., Chase, G.G. (2019). Effect of pore size and wettability of multilayered coalescing filters on water- in-ULSD coalescence. Separation and Purification Technology. 221: p. 236-248.
14
[15] Chen, F., Ji, Z., Qi, Q. (2018). Effect of pore size and layers on filtration performance of coalescing filters with different wettabilities. Separation and Purification Technology. 201: p. 71- 78.
15
[16] Patra, A.S., Makhija, D., Mukherjee, A.K., Tiwari, R., Sahoo, C.R., Mohanty, B.D. (2016). Improved dewatering of iron ore fines by the use of surfactants. Powder Technology. 287: p. 43-50.
16
[17] Svarovsky, L. (2000). Solid-liquid separation. Elsevier. [18] Dash, M., Dwari, R.K., Biswal, S.K., Reddy, P.S.R., Chattopadhyay, P., Mishra, B.K. (2011). Studies on the effect of flocculant adsorption on the dewatering of iron ore tailings. Chemical Engineering Journal. 173(3): p. 318-325.
17
[19] Niu, M., Zhang, W., Wang, D., Chen, Y., Chen, R. (2013). Correlation of physicochemical properties and sludge dewaterability under chemical conditioning using inorganic coagulants. Bioresource technology. 144: p. 337-343.
18
[20] Besra, L., Sengupta, D.K., Roy, S.K. (2000). Particle characteristics and their influence on dewatering of kaolin, calcite, and quartz suspensions. International Journal of Mineral Processing. 59(2): p. 89-112.
19
[21] Townsend, I. (2003). Automatic pressure filtration in mining and metallurgy. Minerals engineering. 162(2): p. 165-173. [22] Dong, K.J., Zou, R.P., Yang, R.Y., Yu, A.B., Roach, G. (2009). DEM simulation of cake formation in sedimentation and filtration. Minerals Engineering. 22(11): p. 921-930.
20
[23] Ni, L.A., Yu, A.B., Lu, G.Q., Howes, T. (2006). Simulation of the cake formation and growth in cake filtration. Minerals engineering. 19(10): p. 1084-1097.
21
[24] Day, A. (2002). Mining Chemicals Handbook. Revised Edition, CYTEC, Horton Printing Company, Meriden, CT. [25] Mamghaderi, H., Gharabaghi, M., Noaparast, M. (2018). Optimization of the role of physical parameters in the filtration processing with a focus on the fluid flow from the pore. Minerals Engineering. 122: p. 220-226.
22
[26] Castro, S., and Laskowski, J.S. (2015). Depressing effect of flocculants on molybdenite flotation. Minerals Engineering. 74: p. 13-19.
23
[27] Fan, Y., Dong, X., Li, H. (2015). Dewatering effect of fine coal slurry and filter cake structure based on particle characteristics. Vacuum. 114: p. 54-57.
24
[28] Wang, L.F., He, D.Q., Tong, Z.H., Li, W.W., Yu, H.Q. (2014). Characterization of dewatering process of activated sludge assisted by cationic surfactants. Biochemical Engineering Journal. 91: p. 174-178.
25
[29] Lihong, D.U., Xu, C.H.E.N., Wenping, L.I., Qixin, Z.H.U. (2011). A study on enhancement of filtration process with filter aids diatomaceous earth and wood pulp cellulose. Chinese Journal of Chemical Engineering. 19(5): p. 792-798.
26
[30] Jabri, W., Vroman, P., Perwuelz, A. (2015). Study of the influence of synthetic filter media compressive behavior on its dust holding capacity. Separation and Purification Technology. 156: p. 92-102.
27
[31] Stickland, A.D., Irvin, E.H., Skinner, S.J., Scales, P.J., Hawkey, A., Kaswalder, F. (2016). Filter Press Performance for Fast‐Filtering Compressible Suspensions. Chemical Engineering & Technology. 39(3): p. 409-416.
28
[32] Lee, K.M., Jo, Y.M., Lee, J.H., Raper, J.A. (2008). Assessment of surface and depth filters by filter quality. Powder Technology. 182(2): p. 187-194.
29
[33] Zhang, Y., Gong, G., Wu, G., Wang, Y. (2014). Physical properties and filter cake structure of fine clean coal from flotation. International Journal of Mining Science and Technology. 24(2): p. 281-284.
30
ORIGINAL_ARTICLE
The role of Mineral Salts Company in pollution of Mighan playa sediments with heavy metals by contamination indices and multivariate analysis methods, Arak, Iran
Heavy metal concentrations were investigated for 30 sediments collected from different regions of Mighan playa/lake. The means of Cr, Cu, Ni, Pb, Zn and Cd in sediments of playa/lake were much lower than the soil guideline values of Iran and background values of region (BVR). However, the maximum concentrations of Cr, Cu, Ni, Pb, Zn and Cd were higher than BVR. Only 7% of Cr, Cu, Zn, 27% of Ni, 14% of Pb and 38% of Cd concentration exceeded the BVR. The heavy metal Cr, Zn, Ni and Cd are the most important metals in different land use. About 11% of the samples in the lake land use contain Cr, Ni, Pb and Zn has concentration higher than BVR. The concentration of Cu and Pb in 33% and 67% samples is exceeded the BVR in Tail of Mineral Salts Company. The spatial distribution patterns of Cr, Cu, Ni, Pb, Zn and Cd were generally similar and increase from Mighan playa/lake to the Arak city and mainly affected by anthropogenic sources. Among the six types of land use, the concentrations of Cr, Ni, Zn and Cd in the range land and wastewater sludge were significantly higher than those in the other land use (p <0.05). In factor analysis, Cr, Ni, Zn in factor1, Cd in factor2, Pb, Zn in factor3 were originated from the municipal sewage, industrial plants activities and Arak urban traffic.
https://ijmge.ut.ac.ir/article_82445_d1aaebfdfb230a1669e1917b5b36f836.pdf
2021-12-01
117
124
10.22059/ijmge.2020.283819.594818
heavy metal pollution
Soil and sediment contamination
Anthropogenic sources
Statistical methods
Mighan playa/lake
ّFeridon
Ghadimi
ghadimi@arakut.ac.ir
1
Department of Mining Engineering, Arak University of Technology, Arak, Iran
LEAD_AUTHOR
Abdolmotaleb
Hajati
am_hajati@arakut.ac.ir
2
Department of Mining Engineering, Arak University of Technology, Arak, Iran
AUTHOR
Akram
Sabzian
sabzian2002@gmail.com
3
Incubator of earth researchers .Arak University of Technology
AUTHOR
[1] Singare, P.U., Bhanage, S.V & Lokhande, R.S.(2011).Study on water pollution along the Kukshet Lakes of Nerul, Navi Mumbai, India with special reference to pollution due to heavy metals. International Journal of Global Environmental,11 (1),79–90.
1
[2] Zhang, H., Jiang, Y., Ding, M & Xie, Z.(2017).Level, source identification, and risk analysis of heavy metal in surface sediments from river-lake ecosystems in Poyang Lake, China. Environmental Science Pollution Research,24(27),21902–21916.
2
[3] Zhengwu, C., Yang, W., Na,Z., Rui,Y., Guanghui,X & Yong, Y. (2018).Spatial distribution and risk assessment of heavy metals in Paddy soils of Yongshuyu irrigation area from Songhua River Basin, Northeast China. Chines Geographical Science,28 (5),797– 809.
3
[4] Ghadimi, F. (2020). Assessing the heavy metal contamination in sediments of Mighan playa using pollution indices. Journal of Stratigraphy and Sedimentology Researches University of Isfahan, 36(78), 21-38(in Persian).
4
[5] Saberinasab, F., & Mortazavi, S. (2018). Evaluation of Pb, Zn, Cu, and Ni concentration in Arak Mighan wetland based on sediment pollution indices. Journal of Water and Soil Science, 22(1), 15-27.
5
[6] Safari Sinegani,A.A., Safari Sinegani,M. (2018). Chemical fractionation and bioavailability of Fe, Mn, Pb, and Cd in soils around Meyghan Lake, Arak, Iran. International Journal of Environmental Science and Technology,16,3297-3308.
6
[7] Sheikh Fakhradins, A., & Abbasnejad, A.(2015). The influence of weathering on hydrochemistry of streams drainage volcanic rocks: Bidkhan stream, southeast of Bardsir in Kerman. Journal of Geography and Planning, 19(53),203-226.
7
[8] Thiombane,M., Lima,A., Albanese,S., Buscher,J.T., & DeVivo, B.(2018). Soil contamination compositional index: A new approach to quantify contamination demonstrated by assessing compositional source patterns of potentially toxic elements in the Campania Region (Italy). Applied Geochemistry, 96, 264-276.
8
[9] Li,F., Huang, J., Zeng, G., Yuan, X., Li, X., Liang, J., Wang, X., Tang, X & Bai, B.(2013).Spatial risk assessment and sources identification of heavy metals in surface sediments from theDongting Lake, Middle China. Journal of Geochemical Exploration, 132,75–83.
9
[10] Shi, X., Chen, L & Wang, J.(2013). Multivariate analysis of heavy metal pollution in street dust of Xianyang city, NW China. Environmental Earth Science,69 (6),1973-1979.
10
[11] Cheng, H., Li,M., Zhao, C., Yang, K., Li, K., Peng,M., Yang, Z., Liu, F., Liu, Y & Bai, R.( 2015).Concentrations of toxic metals and ecological risk assessment for sediments of major freshwater lakes in China. Journal Geochemical Exploration,157, 15–26.
11
[12] Ahmadi Doabi, Sh., Karami, M & Afyuni, M.(2019).Heavy metal pollution assessment in agricultural soils of Kermanshah province, Iran. Environmental Earth Sciences,https://link.springer.com/article/10.1007/s12665-019-8093-7.
12
[13] Imam, N., El-Sayed, M.S & El-Sherif Goher, M.(2020). Risk assessments and spatial distributions of natural radioactivity and heavy metals in Nasser Lake, Egypt. Environmental Science and Pollution Research, https://doi.org/10.1007/s11356-020-08918-7.
13
[14] Beutel, M.W., Leonard, T.M., Dent, S.R & Moor, B.C.(2008). Effects of aerobic and anaerobic conditions on P, N, Fe, Mn, and Hg accumulation in waters overlaying profundal sediments of an Oligo-mesotrophic lake. Water Research, 42,1953-1962.
14
[15] Amor,R.B., Yahyaoui,A., Abidi, M., Chouba,L& Gueddari, M.(2019). Bioavailability and assessment of metal contamination in surface sediments of Rades-Hamam Lif coast, around Meliane river,(Gulf of Tunis, Tunisia, Mediterranean Sea). Journal of Chemistry, Article ID 4284987,11 pages https://doi.org/10.1155/2019/4284987.
15
[16] Violintzis, C., Arditsoglou, A & Voutsa, D.(2009). Elemental composition of suspended particulate matter and sediments in the coastal environment of Thermaikos Bay, Greece: delineating the impact of inland waters and wastewaters. Journal of Hazard Mater, 166,1250 -1260.
16
[17] Malsiu,M., Shehu,I., Stafilov,T & Faiku, F.(2020). Assessment of heavy metal concentrations with fractionation method in sediments and waters of the Badovci lake (Kosovo). Journal of Environmental and Public Health, Article ID 3098594,14 pages https://doi.org/10.1155/2020/3098594
17
[18] Wang, H.D., Fang, F.M & Xie, H.F.(2010). Research situation and outlook on heavy metal pollution in the water environment of China. Guangdong Trace Element Science, 17,14-18.
18
[19] Govil, P.K., Sorlie, J.E., Sujatha, D & Krishna, A.K.(2012). Assessment of heavy metal pollution in lake sediments of Katedan industrial development area, Hyderabad, India. Environmental Earth Science,66(1),121-128.
19
[20] Ghadimi, F & Ghomi, M. (2013). Assessment of the effects of municipal wastewater on the heavy metal pollution of water and sediment in Arak Mighan Lake, Iran. Journal of Tethys, 1(3),205- 214.
20
[21] Ghadimi, F. (2014). Assessment of heavy metals contamination in urban topsoil from Arak industrial city, Iran. Journal of Tethys, 2(3),196–209.
21
[22] Rahimpour-Bonab,H &Abdi,L.(2012). Sedimentology and origin of Meyghan lake/playa deposits in Sanandaj–Sirjan zone, Iran. Carbonates and Evaporites, 27(3-4),375-393.
22
[23] Ghadimi, F.(2014). Assessment of the sources of chemical elements in sediment from Arak Mighan Lake. International Journal Sediment Research, 29,159-170.
23
[24] Ardakani, S., & Jamshidi, K. (2015). Assessment of metals (Co, Ni, and Zn) content in the sediments of Mighan wetland using a geoaccumulation index. Iran of Journal Toxicology, 9 (30), 1386-1390.
24
[25] Safari Sinegani,M, Safari Sinegani,A.A & Hadipour, M. (2018). Sources and spatial distribution of lead (Pb) and cadmium (Cd) in saline soils and sediments of Mighan Playa (Iran). Lake and Reservoir, https://doi.org/10.1111/lre.12208.
25
[26] The United States Environmental Protection Agency (USEPA). (1996). Method 3050B: Acid digestion of sediments, sludges, and soils; Revision 2; USEPA: Washington, DC, USA.
26
[27] Gaudino, S., Galas,Ch.,Belli,M., Barbizzi,S., Zorzi,P ., Jaćimović, R., Jeran,Z., Pati,A & Sansone,U.(2007). The role of different soil sample digestion methods on trace elements analysis: a comparison of ICP-MS and INAA measurement results. Mathematical and Computational Chemistry,12,84-93.
27
[28] Ghadimi F & Ghomi M.(2016). Statistical analysis of exploration geochemical data using a function in Statistica environment, Print: Arak University of Technology Publications, 288 pages (in Persian).
28
[29] Kwon, Y.T &Lee, C.W.(1998). Application of multiple ecological risk indices for the evaluation of heavy metal contamination in a coastal Dredging area. The Science Total Environment, 214,203–210.
29
[30] Håkanson L.(1980). An ecological risk index for aquatic pollution control: A sedimentological approach. Water Research, 14,975– 1001.
30
[31] Caeiro, S., Costa, M.H & Ramos, T.B.(2005). Assessing heavy metal contamination in Sado estuary sediment: An Index Analysis Approach. Ecology Indicative, 5,151–169.
31
[32] Cheng, J.L., Shi, Z& Zhu, Y.W.(2007). Assessment and mapping of environmental quality in agricultural soils of Zhejiang Province, China. Journal Environmental Science, 19, 50–54.
32
[33] Kaiser, H.F.(1960). The application of electronic computers to factor analysis. Education Psychology. Measures, 20,141–151.
33
[34] Loska, K& Wiechuła, D.(2003). Application of principal component analysis for the estimation of the source of heavy metal contamination in surface sediments from the Rybnik reservoir. Chemosphere, 51,723–733.
34
[35] Manly Bryan, F.J.(1994). Multivariate statistical methods: A Primer. 2nd Ed. Chapman and Hall; London, England. 215 p.
35
[36] Lombardo, R & Meulman, J.(2010). Multiple correspondence analysis via polynomial transformations of ordered categorical variables. Journal of Classification, 27(2),191–210.
36
[37] IEO (Iranian Environment Organization). (2018). Iran Quality Resource Quality Standards.
37
[38] CCME (Canadian Council of Ministers of the Environment). (2007). Canadian Soil Quality Guidelines for the Protection of Environmental and Human Health, Canadian Council of Ministers of the Environment, Winnipeg.
38
[39] Taylor,S.R & McLennan,S.M.(1995).The geochemical evolution of the continental crust. Review Geophysics, 33,241–265.
39
[40] GB15618.(1995). Soil Environmental Quality Standards in China, Ministry of Environmental Protection of China, Beijing.
40
[41] Wilcke,W., Muller,S., Kanchanakool, N& Zech,W.(1998). Urban soil contamination in Bangkok: heavy metal and aluminum partitioning in topsoils. Geoderma, 86,211–228.
41
[42] Salonen, V& Korkka-Niemi,K.(2007). Influence of parent sediments on the concentration of heavy metals in urban and suburban soils in Turku, Finland. Applied Geochemistry, 22, 906–918.
42
[43] Gu, Y.G.& Lin, Q.(2016). Trace metals in a sediment core from the largest mariculture base of the eastern Guangdong coast, South China: vertical distribution, speciation, and biological risk. Mars Pollution Bulletin,113,520–525.
43
[43] Wang, Z.Y.(2013). Factor analysis of heavy metals in the sediment in Changping section of the Wenyu river and their pollution assessment. Research of Environmental Sciences, 26(8),838-843.
44
[44] Sheela, A.M., Letha, J., Joseph, S., Joeseph, J & Thomas, J.(2014).Multivariate analysis of the sediment quality of a tropical coastal lake system. Lakes and Reservoirs, 19(4), 306-317.
45
[45] Mortazavi, S & Saberi –Nasab, F.(2016). Survey of Cu and Ni heavy metals pollution in surface sediments of Mighan Arak wetland using pollution and sediment quality indices. International Conference on Civil, Architecture, Urban Management and Environment in the Third Millennium, Rasht (in Persian).
46
[46] Ansarian, F.(2015). Measurement of heavy metal surface sediments in Mighan playa to investigate the effects of Arak wastewater treatment plant. Second Conference on New Findings in the Environment and Agricultural Ecosystems, Tehran University (in Persian).
47
[47] Fazeli, M.Sh & Malaki, Alagh, M.(2000). Investigation of some heavy metals in sediments of Gomishan wetland. Third National Conference on Environmental Health, the Islamic Azad University of Shiraz Branch (in Persian).
48
[48] Babaee, H., Khadaprast, SH &Shavandasht, J.(2013).The impact of industrial, agricultural, and urban wastewater on mineral pollutants in Anzali international wetland. First National Iranian Wetlands Conference, Islamic Azad University of Ahvaz, Iran (in Persian).
49
[49] Pyri, H.(2010). Evaluation of heavy metal concentrations of Ni, Cu, Cd, Hg, and Pb in Hamoon wetland, Second National Wetlands Conference of Iran. Islamic Azad University, Ahvaz Branch, Iran (in Persian).
50
[50] Aghasi, B., Jalalian, A& Khadami, H.(2015). Evaluation of heavy metal contamination in sediments of international Gvkhuni wetland. Second Conference on New Findings in the Environment and Agricultural Ecosystems, the University of Tehran (in Persian).
51
ORIGINAL_ARTICLE
Analysing the Role of Safety Level and Capital Investment in Selection of Underground Metal Mining Method
It is one of the important tasks to select a suitable mining method for economic and safely extraction of the specific ore deposit. The selection of individual mining methods depends on multiple factors like dip, shape, thickness, depth, grade distribution, RMR (rock mass rating) of ore and adjacent strata, and RSS (rock substance strength) of ore and adjacent strata. The present study aims to analyse the role of two extrinsic factors (safety and capital) in the selection of underground metal mining method. A fuzzy-AHP decision making model is developed to analyse the possible changes in the mining method with different levels of safety and capital. The study considers seven alternatives or mining methods (block caving, sublevel stoping, sublevel caving, room and pillar mining, shrinkage stoping, cut and fill stoping and square set stoping) in the model. The results revealed that the preference level or ranking of different mining method in a particular condition like low safety (SAL), medium safety (SAM), high safety (SAH), low capital (CL), medium capital (CM), and high capital (CH) remains same for different decision-making attitude and uncertainty level.
https://ijmge.ut.ac.ir/article_83186_4c11beb6bcdf6d62f0733402b514d100.pdf
2021-12-01
125
131
10.22059/ijmge.2021.313741.594877
Safety
capital
Fuzzy-AHP
Sensitivity analysis
Underground mining methods
Bhanu Chander
Balusa
bhanuchanderbalusa@gmail.com
1
SCOPE, VIT Chennai
LEAD_AUTHOR
[1] Namin ES, Shahriar K, Ataee-Pour M, Dehghani H. A new model for mining method selection of mineral deposit based on fuzzy decision-making . J of the South Afri Inst of Mining and Metallurgy 2008; 108(7): 385–395.
1
[2] Naghadehi MZ, Mikaeil R, Ataei M. The application of fuzzy analytic hierarchy process (FAHP) approach to selection of optimum underground mining method for Jajarm Bauxite Mine, Iran. Exp Sys with Applications 2009; 36(4): 8218– 8226. https://doi.org/10.1016/j.eswa.2008.10.006
2
[3] Mikaeil R, Naghadehi M Z, Ataei M, Khalokakaie R. A decision support system using fuzzy analytical hierarchy process (FAHP) and TOPSIS approaches for selection of the optimum underground mining method. Arch of Min Science 2009; 54(2): 349–368.
3
[4] Alpay S, Yavuz M. A Decision Support System for Underground Mining. In Okuno HG, Ali M, editors. New Trends in Applied Artificial Intelligence Decision Support Systems, Proceedings of the 20th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2007, Kyoto, Japan, June 26–29; 2007, p. 334–343. https://doi.org/10.1007/978-3-540-73325-6_33
4
[5] Gupta S, Kumar U. An analytical hierarchy process (AHP) guided decision model for underground mining method selection. Int J of Min Reclam and Env 2012; 26(4):324–336. https://doi.org/10.1080/17480930.2011.622480
5
[6] Yavuz M. The application of the analytic hierarchy process (AHP) and Yager’s method in underground mining method selection problem. Int J of Min Reclam and Env 2015; 29(6):453–475. https://doi.org/10.1080/17480930.2014.895218
6
[7] Dehghani H, Siami A, Haghi P. A new model for mining method selection based on grey and TODIM methods. J of Min and Env 2017; 8(1): 49–60. http://dx.doi.org/10.22044/jme.2016.626
7
[8] Balusa BC, Gorai AK. Design of a multi-criteria decisionmaking (MCDM) model using fuzzy-AHP for selection of appropriate underground metal mining method. Int j of min and miner eng. (In press).
8
[9] Balusa BC, Gorai AK. Sensitivity analyzis of fuzzy-analytic hierarchical process (FAHP) decision-making model in selection of underground metal mining method. J of Sust Min 2018. https://doi.org/10.1016/j.jsm.2018.10.003
9
[10] Balusa BC, Gorai AK. A Comparative Study of Various Multicriteria Decision-Making Models in Underground Mining Method Selection. J of the Inst of Engineers (India): Series D 2018; 1-17. https://doi.org/10.1007/s40033-018-0169-0.
10
[11] Balusa BC, Singam J. Underground Mining Method Selection Using WPM and PROMETHEE. Journal of The Institution of Engineers (India): Series D 2018; 99:165-171. https://doi.org/10.1007/s40033-017-0137-0.
11
[12] Stebbins Scott A, Otto LS. Cost estimating for underground mines. Hustrulid WA, Bullock RL, editors. Underground mining methods: Engineering fundamentals and international case studies, USA: SME; 2001, p.49-72.
12
[13] Hartman HL, Mutmansky JM. Introductory mining engineering. 2nd John Wiley & Sons; 2002.
13
[14] Tatiya RR. Surface and underground excavations: methods, techniques, and equipment. 2nd ed. CRC Press; 2013.
14
[15] Saaty TL. The analytic hierarchy process: planning, priority setting, resources allocation. McGraw-Hill; 1980.
15
[16] Gorai AK, Kanchan, Upadhyay A, Tuluri F, Goyal P, Tchounwou PB. An innovative approach for determination of air quality health index. Science of the Total Environment 2015; 533: 495–505. https://doi.org/10.1016/j.scitotenv.2015.06.133.
16
[17] Lee AR. Application of modified fuzzy AHP method to analyze bolting sequence of structural joints (Doctoral Dissertation). Lehigh University Bethlehem, PA, USA.1995. [18] Azadeh A, Osanloo M, Ataei-Pour M. A new approach to mining method selection based on modifying the Nicholas technique. Applied Soft Computing 2010; 10(4): 1040-1061. https://doi.org/10.1016/j.asoc.2009.09.002.
17
ORIGINAL_ARTICLE
Fastest Modified Model of Hardy Cross Method for Ventilation Network Analysis of Mines (Second Conflation Model)
Ventilation network design is done in manual and computerized methods. Computerized method is based on mathematical approximate methods. Several algorithms were presented in mathematical approximate methods for analyzing of water distribution and ventilation networks. Hardy Cross method is the most commonly model of mathematical approximate method for analyzing of ventilation networks in mine. For faster convergence to at the final result of Hardy Cross method were presented other models such as Wang model, conflation model and Newtonian models (First, third and sixteenth). In this paper is performed an initial review of Hardy Cross method and its modified models. Then first, third, and sixteenth modified models of Newtonian are presented for more accurate analysis of ventilation networks in mines. Finally, second conflation model will be presented as the fastest modified model of Hardy Cross method to achieve at the final result.
https://ijmge.ut.ac.ir/article_84221_b5e382c31cb5fad52888d5006db1630f.pdf
2021-12-01
133
143
10.22059/ijmge.2020.281156.594808
ventilation
Hardy Cross
conflation model
Newtonian models
Ebrahim
Elahi Zeyni
elahi@eng.usb.ac.ir
1
Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
LEAD_AUTHOR
Farhang
Sereshki
farhang@gmail.com
2
Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
AUTHOR
Reza
Khaloo Kakaie
r_kakaie@yahoo.com
3
Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
AUTHOR
[1] Cross, H. (1936). Analysis of Flow in Networks of Conduits or Conductors. Bulletin 286, Engineering Experiment Station, University of Illinois, Urbane, 29 pp.
1
[2] Collins, M., Cooper, L., Helgason, R., Kennington, J., and LeBlanc, L. (1978). Solving the Pipe Network Analysis Problem Using Optimization Techniques. Management Science, Vol. 24, pp. 747-760.
2
[3] Chiplunkar, AV., Mehndiratta, SL., and Khanna, P. (1990). Analysis of looped water distribution networks. Environ Softw, 5(4): 202–6.
3
[4] Pramod R. Bhave. (1991). Analysis of Flow in Water Distribution Networks. Technomic Pub. Co. [5] Gupta, I., Bassin, JK., Gupta, A., and Khanna, P. (1993). Optimization of water distribution system. Environ Softw, 8(2): 101–13.
4
[6] Eiger, GU., Shamir, U., and Ben-Tal, A. (1994). Optimal design of water distribution networks. Water Resour Res, 30(9): 2637–46.
5
[7] Basha, HA., and Kassab, BG. (1996). Analysis of water distribution systems using a perturbation method. Appl Math Model, 20(4):290–7.
6
[8] Dandy, G. C., Simpson, A. R., and Murphy, L. J. (1996). An improved genetic algorithm for pipe network optimization. Water Resources Research 32 (2), 449-458.
7
[9] Savic, D.A., and Walters, G.A. (1997). Genetic algorithms for least-cost design of water distribution networks. Journal of Water Resources Planning and Management-ASCE 123 (2), 67-77.
8
[10] Gupta, I., Gupta, A., and Khanna, P. (1999). Genetic algorithm for optimization of water distribution systems. Environmental Modelling & Software 14 (5)- 437e446.
9
[11] Todini, E. (2000). Looped water distribution networks design using a resilience index based heuristic approach. Urban Water, 2(2):115–22.
10
[12] Arsene, CTC., Bargiela, A., and Al-Dabass, D. (2004). Modelling and simulation of water systems based on loop equations. Int J Simul, 5(1-2): 61–72.
11
[13] Zecchin, AC., Simpson, AR., Maier, HR., Leonard, M., Roberts, AJ., and Berrisford, MJ. (2006). Application of two ant colony optimisation algorithms to water distribution system optimisation. Math Comput Model, 44(4-5):451–68.
12
[14] Boulos, PF., Lansey, KE., and Karney, BW. (2006). Comprehensive water distribution systems analysis handbook for engineers and planners. 2nd. MWH: Hardback. [15] Giustolisi, O. (2010). Considering actual pipe connections in water distribution network analysis. Journal of Hydraulic Engineering, Vol. 136, No. 11, 889-900.
13
[16] Ayad, A., Awad, H., and Yassin, A. (2013). Developed hydraulic simulation model for water pipeline networks. Alexandria Eng, J., 52: 43–49. [17] Moosavian, N. and Jaefarzadeh, M. R. (2014a). Hydraulic analysis of water distribution network using shuffled complex evolution. Journal of Fluids , Vol. 2014, 1-12.
14
[18] Moosavian, N., and Jaefarzadeh, M. R. (2014b). Hydraulic Analysis of Water Supply Networks Using a Modified Hardy Cross Method. International Journal of Engineering, Vol. 27, No. 9, 1331-1338.
15
[19] Boanoa, F., Scibettab, M., Ridolfia, L., and Giustolisic, O. (2015). Water distribution system modeling and optimization: a case study. Procedia Engineering, 119, 719 – 724
16
[20] Creacoa, E., and Franchinib, M. (2015). The identification of loops in water distribution networks. Procedia Engineering, 119, 506 – 515.
17
[21] Singh, M., Kheer, S.K., and Pandita, I.K. (2016). Improvement of water distribution networks analysis by topological similarity, Alexandria Engineering Journal, 55, 1375–1383.
18
[22] Jha, K., and Mishra, M.K. (2018). “Modified Newton-Raphson Technique for Integrated Object-Oriented Water Pipe Network Analysis”, 1st International WDSA / CCWI , Joint Conference, Kingston, Ontario, Canada, 23-25.
19
[23] Wang, Y. J. (1982). Ventilation Network Theory. Mine Ventilation and Air Conditioning. 2nd ed., H. L. Hartman (Ed.), Wiley-Interscience, NY, pp. 167-195.
20
[24] Wang, Y. J. (1982). Critical Path Approach to Mine Ventilation Networks with Controlled Flow. Trans. SMEAIME, Vol. 272, pp. 1862-72. [25] Wang, Y. J. (1984). A Non-Linear Programming Formulation for Mine Ventilation Networks with Natural Splitting. International Journal of Rock Mechanics and Mining Science, Vol. 21, No. 1, pp. 42-3-45.
21
[26] Wang, Y. J. (1989). “A Procedure for Solving A More Generalized System of Mine Ventilation Network Equations”. Proceedings of the 4th US. Mine Ventilation Symposium, SME, Littleton, Co., pp. 419-424.
22
[27] Bhamidipati, S. S., and Procarione, J. A. (1985). Linear Analysis for the Solution of Flow Distribution Problems. Proceedings of the 2nd US Mine Ventilation Symposium, Mousset_Jones, P. (Ed.), Rotterdam, Netherlands, pp. 645- 654.
23
[28] Hu, W., and Longson, I. (1990). The Optimization of Airflow Distribution in Ventilation Networks Using A Nonlinear Programming Method. Mining Science and Technology, Vol. 10, No. 2, pp. 209-219.
24
[29] Kamba, G. M., Jacques, E., and Patigny, J. (1995). Application of the Simplex Method to the Optimal Adjustment of the Parameters of A Ventilation Network. Proceedingss of the 7th US Mine Ventilation Symposium, Wala, A. M. (Ed.), SME, Littleton, Co., pp. 461-465.
25
[30] Madani, H. (2003). Mines Ventilation. Vol. 2, Tehran: Amirkabir University of Technology (Tehran Polytechnic) Press.
26
[31] Madani, H. (2006). Mines Ventilation. Vol. 1, Print 5, Tehran: University Center Pub.
27
[32] Khaled Ali El-Nagdy. (2008). Analysis of Complex Ventilation Networks in Multiple Fan Coal Mine. Ph.D thesis, West Virginia University.
28
[33] Brkic, D. (2009). An improvement of Hardy Cross method applied on looped spatial natural gas distribution networks. Applied Energy, Vol. 86, pp. 1290-1300.
29
[34] Elahi, E. and Rabienejad, H. (2012a). Designing ventilation of west Razmja coal mine of Eastern Alborz Coal Mines Company using Ventsim software. 1st Iranian coal congress , Shahrood University of Technology, pp. 1-11.
30
[35] Elahi, E. and Rabienejad, H. (2012b). Selection of main ventilation fan of Kalariz coal mine in Eastern Alborz Coal Mines Company using Ventsim software. 1st Iranian Mining Technologies Conference, pp. 333-343.
31
[36] Elahi, E. (2014). The Effect of Natural Ventilation on Coal Mine of Western Zemestan Yourt by Ventsim Software. 2nd Iranian coal congress, Shahrood University of Technology, pp. 1-7.
32
[37] Elahi, E. (2014). Designing Ventilation in Underground Mining by Manual Method (Case Study: Coal Mine of Takht). 5st mining engineering congress , Tehran, Iran, pp. 457-463.
33
[38] Elahi, E. (2014). The Principles of Designing Ventilation in Mines. Publication of Jihad Amikabir University.
34
[39] Elahi, E. (2014). The Improvement of Hrady Cross Method in the Analysis of Underground Excavations Ventilation Network. Tunneling and Underground Space Engineering, pp. 101-117.
35
[40] Elahi, E., Zeinaly, H. and Amini, E. (2016). Ventilation Design of Big Mine by Ring Method.1st engineering congress of Mining, Metals and Material, Iran, pp. 1-11.
36
[41] Elahi, E., Faalian, Kh. and Shakoor Shahabi. R. (2016). Selection of Main Fan for Ventilation Network of Coal Mine of Tunnel 8 using Ventsim Software. 3rd National Iranian coal congress, Shahrood University of Technology, pp. 1-7.
37
[42] Ghazvini, M.R. and Elahi, E. (2018). Selection of Main Fan for Ventilation Network of Coal Mine of Central Kalariz using Ventsim Software. 4th National Iranian coal congress , Shahrood University of Technology, pp. 1-8.
38
[43] Darvishi, M. and Barati, A. (2007). A third-order Newtontype method to solve systems of nonlinear equations. Applied Mathematics and Computation, Vol. 187, No. 2, 630-635.
39
[44] Li, X., Mu, C., Ma, J. and Wang, C. (2010). Sixteenth-order method for nonlinear equations, Applied Mathematics and Computation, Vol. 215, No. 10, 3754-3758.
40
[45] www.sibenergomash.com.
41
ORIGINAL_ARTICLE
Effect of Rock Joint on Boreability of TBM at Northern Section of Kerman Water Conveyance Tunnel
Nowadays, Tunnel Boring Machines (TBM) are widely used around the world on account of their high rate of excavation, little impact on the surrounding rock and their high safety standards. The rock mass boreability is considered as one of the main parameters in evaluating the TBMs performance in jointed rock masses .Boreability is a parameter reflecting the interaction between the rock mass and cutting tools. This paper aims to render an account of the effect of Joints Geometrical Parameters on the boreability by use of a database prepared utilizing the data (TBM operation and geological parameters) collected from Kerman Water Conveyance Tunnel projects in Iran. For this purpose, the joint parameters (orientation, spacing, persistence) affecting the boreability have initially been investigated. Then, the total fracturing factors (Bruland) and Persistence classification were used to investigate the effects of all three parameters on the borability. The results showed that the boreability is also increased by increasing the joint persistency. Besides, the effect of fracturing factor ( ) on boreability increases by increasing the joint persistency. In this paper, a new parameter called "Rock Joint Index"(RJI) is also presented according to the analysis performed on the database. The boreability value estimated based on the RJI shows a good agreement with the actual penetration rates.
https://ijmge.ut.ac.ir/article_84222_46077338f4ba5dedcd6291dc30414984.pdf
2021-12-01
145
150
10.22059/ijmge.2020.288862.594825
Joint Geometrical Parameters
Jointed rock mass
Penetration Rate
Rock Mass Boreability
Tunnel Boring Machines (TBM)
Morteza
Khosravi
mortezakhosravi80@gmail.com
1
Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology
LEAD_AUTHOR
Ahmad
Ramezanzadeh
aramezanzadeh@shahroodut.ac.ir
2
Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology
AUTHOR
Zare
Shokrollah
zare@shahroodut.ac.ir
3
Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology
AUTHOR
[1] Wilfing, L. (2016). The Influence of Geotechnical Parameters on Penetration Prediction in TBM Tunnelling Hard Rock. Tehnische chnische university MÜNCHEN.
1
[2] Entacher, M. (2013). Measurement and interpretation of disc cutting forces in mechanized tunneling. Thesis, Technische Universität München. [3] Gong, Q., Zhao, J. (2005). Numerical modeling of the effects of joint orientation on rock Fragmentation by TBM cutters. Tunnelling and underground space technology, Vol 20, PP I3-191.
2
[4] Paltrinieri, E. (2015). Analysis of TBM tunnelling performance in faulted and highly fractured rocks. Doctor of Philosophy thesis, EPFL.
3
[5] Sungong, C., Seungoong, L. (2015). Numerical Study to Estimate the Cutting Power on a Disc Cutter in Jointed Rock Mass. KSCE Journal of Civil Engineering.
4
[6] Gong, R., Jiao, Y. (2006). Numerical simulation of influence of joint spacing on rock Fragmentation by TBM cutters. Tunnelling and Underground Space Technology, 21 (1), 46–55.
5
[7] Sharifzadeh, M., Iranzadeh, A. (2009). Threedimensional numerical modelling of joint spacing and orientation effects on rock cutting process by a single TBM. Published in CIM Magazine, Vol 4, No 6.
6
[8] Bejari, H., Ataei, M., Khademi, J. (2011). Simultaneous effects of joint spacing and joint orientation on the penetration rate of a single disc cutter. Mining Science and Technology (China) 21(4):507-512.
7
[9] Bejari, H., Khademi, J. (2013). Simultaneous Effects of Joint Spacing and Orientation on TBM Cutting Efficiency in Jointed Rock Masses. Rock Mech Rock Eng, 46, 897–907.
8
[10] SOI Company. (2016). Engineering geological report of Kerman water conveyance tunnel. Unpublished report. [11] Bruland, A. (1998). Hard Rock Tunnel Boring. Doctoral Thesis, Norwegian Institute of Technology, Trondheim
9
[12] Macias, F. (2016). Hard Rock Tunnel Boring Performance Predictions and Cutter Life Assessments. Thesis for the degree of Philosophiae Doctor, Norwegian University of Science and Technology.
10
[13] Bieniawski, Z.T. (1984). Rock mechanics design in mining and tunneling. A.A. Balkema, Rotterdam, 272 p. [14] Barton, N. (2000). TBM Tunneling in Jointed and Fault Rock. Rotterdam: Balkema
11
[15] Yagiz, S. (2002). Development of rock fracture and brittleness indices to quantify the effects of rock mass features and toughness of the CMS model basic penetration for hard rock tunnel machines. Thesis Doctor of Philosophy, CSM.
12
[16] Yagiz, S. (2008). Utilizing rock mass properties for predicting TBM performance in hard rock conditions. Tunnel Underground Space Technology, 23(3):326–39.
13
[17] Ramezanzadeh, A., Rostami, J., Tadic, D. (2005). Influence of rock mass properties on Performance of hard rock TBMs. RETC PROCEEDINGS, Chapter 56.
14
[18] Ramezanzadeh, A., Rostami, J., Tadic, D. (2008). Effect of Rock Mass Characteristics on Hard Rock Tunnel Boring Machine Performance. 13th Australian Tunnelling Conference, Melbourne, VIC, 4 - 7 May.
15
[19] Gong, QM., Zhao, J. (2009). Development of a rock mass characteristics model for TBM penetration rate prediction. Int J Rock Mech Mining Sci 46(1):8–18.
16
[20] Khademi, J., Shahriar, K., Rezai, B., Rostami, J. (2010). Performance prediction of hard rock TBM using Rock Mass Rating (RMR) system. Tunnelling and Underground Space Technology, No. 25, pp. 333–345.
17
[21] Hassanpour, J. (2010). Analysis of actual TBM performance in Ghomrood project. Bulletin of Iranian Tunneling Association, No. 9, (article in Persian).
18
[22] Hassanpour, J., Rostami, J., Zhao, J. (2011). A new hard rock TBM performance prediction model for project planning. Tunnelling and Underground Space Technology, No. 26, pp. 595–603.
19
[23] Farrokh, E. (2012). Study of utilization factor and advance rate of hard rock TBMS. Doctor of Philosophy, Pennsylvania State University.
20
[24] Zare Naghadehi1, M., Ramezanzadeh, A. (2016). Models for estimation of TBM performance in granitic and mica gneiss hard rocks in a hydropower tunnel. Bull Eng Geol Environ.
21
[25] Rasouli, M. (2018). Rock Joint Rate (RJR); a new method for performance prediction of tunnel boring machines (TBMs) in hard rocks. Tunnelling and Underground Space Technology, 73 261–286.
22
ORIGINAL_ARTICLE
Resilience estimation of the mining fleet (Case study: Sungun copper mine)
In recent years, using of the resilience concept has been increased in order to evaluate the response of systems against the failures. Resilience depicts the system ability to return to its normal operational status after failure accruing. According to the literature survey, there are various studies, which have been done in the field of engineering and non-engineering systems, and there is no study about applying resilience concept in the field of mining industry. In this paper, at first, resilience concept has been introduced and then the resilience of the mining fleet of Sungun copper mine has been estimated. Systems performance indicators include reliability; maintainability and supportability have been used in order to resilience estimation. The results showed that the resilience of the entire system for one hour of its function is equal to 83.1% and this value decreases to 37.1% after 10 hours. This means if there is a failure in the system; it will have 83.1% and 37.1% probabilities to be resilience against the failure event after 1 hour and 10 hours of system function.
https://ijmge.ut.ac.ir/article_84223_85b64a13f2cdbac910b7b26b79542b55.pdf
2021-12-01
151
156
10.22059/ijmge.2021.290468.594828
maintainability
mining
reliability
resilience
Supportability
Adel
Motahedi
adelmotahedi@gmail.com
1
Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
AUTHOR
Farhang
Sereshki
farhang@shahroodut.ac.ir
2
Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
AUTHOR
Mohammad
Ataei
ataei@shahroodut.ac.ir
3
Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
LEAD_AUTHOR
Abbas
Barabadi
abbas.b.abadi@uit.no
4
Department of Engineering and Safety, UiT the Arctic University of Norway, Tromsø, Norway
AUTHOR
Ali
Nouri Qarahasanlou
alinoorimine@gmail.com
5
Faculty of Technical and Engineering, Imam Khomeini International University, Qazvin, Iran
AUTHOR
[1] Yoon, J. T., Youn, B. D., Yoo, M., & Kim, Y. (2017). A newly formulated resilience measure that considers false alarms, 167, 417–427.
1
[2] Alexander, D. E. (2013). Resilience and disaster risk reduction: an etymological journey, 13, 2707–2716.
2
[3] MacKinnon, D., & Derickson, K. D. (2012). From resilience to resourcefulness: A critique of resilience policy and activism, 37(2), 253–270.
3
[4] National Infrastructure Advisory Council (NIAC). (2009). Critical Infrastructure Resilience: Final Report and Recommendations. Department of Homeland Security, U.S.: National Infrastructure Advisory Council.
4
[5] Orwin, K. H., & Wardle, D. A. (2004). New indices for quantifying the resistance and resilience of soil biota to exogenous disturbances, 36, 1907–1912.
5
[6] Allenby, B., & Fink, J. (2005). Toward inherently secure and resilient societies, 390, 1034–1036.
6
[7] Haimes, Y. Y. (2009). On the definition of resilience in systems, 29(4), 498–501.
7
[8] Youn, B. D., Hu, C., & Wang, P. F. (2011). Resilience-driven system design of complex engineered systems, 133, 1–15.
8
[9] Pregenzer, A. L. (2011). Systems resilience: a new analytical framework for nuclear nonproliferation. New Mexico, Albuquerque: Sandia National Laboratories.
9
[10] Ayyub, B. M. (2014). Systems resilience for multihazard environments: definition, metrics, and valuation for decision making, 34(2), 340–355.
10
[11] Hosseini, S. M., Barker, K., & Ramirez-Marquez, J. E. (2016). A Review of Definitions and Measures of System Resilience, 145, 47–61.
11
[12] Holling, C. S. (1973). Resilience and stability of ecological systems, 4, 1–23.
12
[13] Taşan-Kok, T., Stead, D., & Lu, P. (2013). Conceptual Overview of Resilience: History and Context. In A. Eraydin & T. Taşan-Kok (Eds.), Resilience Thinking in Urban Planning (Vol. 106, pp. 39–51). Springer, Dordrecht.
13
[14] Haddadi, P., & Besharat, M. A. (2010). Resilience, vulnerability, and mental health, 5, 639–642.
14
[15] Li, J., & Xi, Z. (2014). Engineering Recoverability: A New Indicator of Design for Engineering Resilience (pp. 1–6). Presented at the Proceedings of the ASME 2014 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Buffalo, New York, USA.
15
[16] Bruneau, M., Chang, S. E., Eguchi, R. T., Lee, G. C., O’Rourke, T. D., Reinhorn, Winterfeldt, D. (2003). A framework to quantitatively assess and enhance the science of the seismic resilience of communities, 19(4), 733–752.
16
[17] Tierney, K., & Bruneau, M. (2007). Conceptualized and Measuring Resilience, 250, 14–17.
17
[18] Cimellaro, G. P., Reinhorn, A., & Bruneau, M. (2010). Seismic resilience of a hospital system, 6, 127–177.
18
[19] Rød, B., Barabadi, A., & Gudmestad, O. (2016). Characteristics of Arctic Infrastructure Resilience: Application of Expert Judgement. Presented at the 26th International Ocean and Polar Engineering Conference, 26 June-2 July, Rhodes, Greece: International Society of Offshore and Polar Engineers.
19
[20] Cai, B., Xie, M., Liu, Y., & Feng, Q. (2018). Availability-based engineering resilience metric and its corresponding evaluation methodology, 172, 216–224.
20
[21] Barabadi, A., Barabady, J., & Markeset, T. (2011). A methodology for throughput capacity analysis of a production facility considering environment condition, 96, 1637–1646.
21
[22] Hoseinie, S. H., Ataei, M., Khalokakaie, R., Ghodrati, B., & Kumar, U. (2012). Reliability analysis of drum shearer machine at mechanized longwall mines, 18(1), 98–119.
22
[23] Garmabaki, A. H. S., Ahmadi, A., Block, J., Pham, H., & Kumar, U. (2016). A reliability decision framework for multiple repairable units, 150, 78–88.
23
[24] Qarahasanlou, A. N., Barabadi, A., & Ayele, Y. Z. (2018). Production performance analysis during operation phase: A case study. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 232(6), 559– 575. https://doi.org/10.1177/1748006X17744383
24
[25] Gharahasanlou, A., Ataei, M., Khalokakaie, R., Barabadi, A., & Einian, V. (2017). Risk-based maintenance strategy: a quantitative approach based on time-to-failure model, 30, 1–10.
25
[26] Nouri Qarahasanlou, A., Ataei, M., Khalokakaie, R., & Mokhberdoran, M. (2018). Mining Spare Parts Provision by Reliability Analysis, Case Study: Sungun Copper Mine, 8(15), 25–38.
26
[27] Adibi, N., Ataee-pour, M., & Rahmanpour, M. (2015). Integration of sustainable development concepts in open pit mine design. Journal of Cleaner Production, 108, 1037–1049. https://doi.org/10.1016/j.jclepro.2015.07.150.
27
ORIGINAL_ARTICLE
Increasing final concentrate grade of the Sarcheshmaeh Copper Complex floatation circuit by flowsheet modification
The Sarcheshmeh copper complex flotation circuit of plant No.1 consists of two identical north and south sections where each includes four rougher (each bank consists of 14 cells), two cleaners (each bank consists of 8 cells), two recleaners (each bank consists of 2 cells), and two scavenger banks (each bank consists of 10 cells). The reduction of feed grade along with a change in the mineralogical composition is the main reason of current lower concentrate grade (24%) compared with the design concentrate grade (32%). Because of a lower feed grade, the amount of rougher concentrate has decreased which in turn has significantly reduced the feed rate to the cleaner, recleaner, and scavenger banks. This has increased the mean residence time of the material in the cleaner section resulting in a lower final concentrate grade. A laboratory study showed that the final concentrate grade can be increased by 2-4% if one cleaner stage is added to the flotation circuit. In this research, based on the laboratory results, one cleaning stage was added to the current flotation circuit. In order to make this modification instrumentally possible in the plant, the final concentrate was gravity transported to the Mo-Cu thickeners. This released two pumps and associated tanks which made the addition of one cleaning stage practicable. Finally, a part of the cleaner and scavenger cells was used as the third cleaning stage. This decreased the residence time in the cleaner and scavenger banks. After implementation of one cleaning stage in the south section of the plant, the performance of the circuit compared with the identical north section. It was found that at the same recovery, the concentrate grade of the south section increased by 2.5%. The promising results led to the implementation of adding one cleaning stage in all sections of the flotation circuit.
https://ijmge.ut.ac.ir/article_84224_73ca1c9382612538fc05917005ce1283.pdf
2021-12-01
157
160
10.22059/ijmge.2021.292375.594833
Flotation
Third cleaning
Gravity transport
Residence Time
Gholamabbas
Parsapour
g.parsapour@vru.ac.ir
1
Mineral Processing Group, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
LEAD_AUTHOR
Hamid
Bidshahri
bidshahri@kmpc.ir
2
Kashigar Mineral Processing Research Center, Shahid Bahonar University of Kerman, Kerman, Iran
AUTHOR
Mousa
Pourkani
pourkani@kmpc.ir
3
Kashigar Mineral Processing Research Center, Shahid Bahonar University of Kerman, Kerman, Iran
AUTHOR
MohammadJavad
Rajabi
rajabi@kmpc.ir
4
Kashigar Mineral Processing Research Center, Shahid Bahonar University of Kerman, Kerman, Iran
AUTHOR
Ehsan
Arghavani
arghavani@kmpc.ir
5
Kashigar Mineral Processing Research Center, Shahid Bahonar University of Kerman, Kerman, Iran
AUTHOR
Gholamabbas
Mohammadian
mohammadian@nicico.ir
6
Sarcheshmeh Copper Complex, Rafsanjan, Iran
AUTHOR
Samad
Banisi
banisi@mail.uk.ac.ir
7
Mining Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran
AUTHOR
[1] Wills B. A. and Finch J. A., "Chapter 12 - Froth Flotation," in Wills' Mineral Processing Technology (Eighth Edition) , B. A. Wills and J. A. Finch Eds. Boston: Butterworth-Heinemann, 2016, pp. 265-380.
1
[2] Radmehr Vahid, Shafaei Sied Z, Noaparast Mohammad, and Abdollahi Hadi, "Optimizing Flotation Circuit Recovery by Effective Stage Arrangements: A Case Study," Minerals, no. 8(10), pp. 1-14, 2018, DOI: https://doi.org/10.3390/min8100417.
2
[3] Ghobadi P., Yahyaei M., and Banisi S., "Optimization of the performance of flotation circuits using a genetic algorithm oriented by process-based rules," International Journal of Mineral Processing, vol. 98, no. 3, pp. 174-181, 2011/03/09/ 2011, DOI: https://doi.org/10.1016/j.minpro.2010.11.009.
3
[4] Banisi S., Kargar A., Pourkani M., Sarvi M., and Hamidi D., "Recent changes at the Sarcheshmeh copper mine flotation circuit," presented at the 33 rd Annual Canadian Mineral Processors Operators Conference, Ottawa, Canada, January 23- 25, 2001.
4
[5] Banisi S., Sarvi M., Hamidi D., and Fazeli A., "Flotation circuit improvements at the Sarcheshmeh copper mine," Mineral Processing and Extractive Metallurgy, vol. 112, pp. 198-206, 2003, DOI: 10.1179/037195503225003681.
5
[6] Hamidi D., "Optimization of reagent addition to rougher flotation cells of the Sarcheshmeh concentration plant," M.Eng. Thesis, Mining Engineering Department, Shahid Bahonar University of Kerman, Kerman, 2001.
6
[7] Rajabi M.J., "Process auditing of the flotation circuit of the Sarcheshmeh copper complex," M.Eng. Thesis, Mining Engineering Department, Shahid Bahonar University of Kerman, Kerman, 2016.
7
[8] Poorkani M., "Investigation the possibility of increasing the copper grade of final concentrate of the Sarcheshmeh copper complex by adding a cleaner stage," R&D Section, Sarcheshmeh Copper Complex, 2014.
8
[9] AGAR G.E., "Optimizing the design of flotation circuits," CIM Bulletin, vol. 73, 824, pp. 173-181, 1989. [10] Sutherland D. N., "A study on the optimization of the arrangement of flotation circuits," International Journal of Mineral Processing, vol. 7, no. 4, pp. 319-346, 1981/01/01/ 1981, DOI: https://doi.org/10.1016/0301-7516(81)90027-2.
9
[11] Banerjee P. K., Gupta A. K., Mukherjee A. K., Das P., Singh N. P., and Singh R. S., "Optimization of Reagents Distribution Down a Coal Flotation Bank to Improve the Recovery of Coarser Particles," Coal Preparation, vol. 27, no. 1-3, pp. 39-56, 2007/06/06 2007, DOI: 10.1080/07349340701249711.
10
[12] Bulatovic S. M., "12 - Flotation of Copper Sulfide Ores," in Handbook of Flotation Reagents , S. M. Bulatovic Ed. Amsterdam: Elsevier, 2007, pp. 235-293.
11
[13] Runge K.C., "Particle Size Distribution Effects that Should be Considered when Performing Flotation Geometallurgical Testing," presented at the second ausimm international geometallurgy conference, Carlton, VIC, Australia, 30 September-2 October 2013.
12
[14] Sao José F. and Pereira C., "Evaluation of Reagents Dispersing for Sphalerite and Galena Particles System," presented at the IMPC2014, Santiago, Chile, 20-24 October 2014, C1316.
13
[15] Tamara M., Valentin C., Tatiana I., and Nadezhda G., "New Reagent Modes for Selective Flotation of Gold-Sulfide Minerals from Refractory Ores," presented at the IMPC2012, New Delhi, India, 23-28 September 2012, 344.
14
[16] Vianna S., "The Effect of Particle Size, Collector Coverage, and Liberation on the Floatability of Galena Particles in an Ore,"Ph.D. Thesis., Juius Kruttschnitt Mineral Research Center, Brisbane, 2004.
15
[17] Bazin C. and Proulx M., "Distribution of reagents down a flotation bank to improve the recovery of coarse particles," International Journal of Mineral Processing, vol. 61, no. 1, pp. 1-12, 2001/01/01/ 2001, DOI: https://doi.org/10.1016/S0301-7516(00)00022-3.
16
[18] Fosu S., Pring A., Skinner W., and Zanin M., "Characterisation of coarse composite sphalerite particles with respect to flotation," Minerals Engineering, vol. 71, pp. 105-112, 2015/02/01/ 2015, DOI: https://doi.org/10.1016/j.mineng.2014.08.023.
17
[19] Matveeva T.N., Chanturiya V.A., Ivanova T.A., and Gromova N.K., "New Reagent Modes for Flotation Recovery of Gold from Refractory," presented at the IMPC 2016, Quebec City, Canada, 11-15 September 2016.
18
[20] Mular M.A. and Veloo C., "Circuit Modifications at Westmin Resources Myra Falls Operations," presented at the Proceedings 24th Annual Meeting of the CMP, Canada, 1992, 26.
19
[21] Shannon L.K. and Trahar W.J., "The Role of Collector in Sulfide Ore Flotation," in Advances in mineral processing. Colorado: SME, 1986, pp. 408-425.
20
[22] Venter C. P.E. and Van Loggerenberg C., "Modifications to the coal-preparation circuit at the Grootegeluk Coal Mine to improve its efficiency," Journal of the Southern African Institute of Mining and Metallurgy, vol. 92, pp. 53-61, Feb. 1992 1992.
21
[23] Yarahmadi M. and Banisi S., "Movazen: A mass balancing software," presented at the Chemical Engineering Congress of Iran, Tehran, 1998.
22
ORIGINAL_ARTICLE
Integration and analysis of geological, geochemical and remote sensing data of south of Neyshabur using principal component analysis
Lack of the existence of known mineral prospects in the preliminary stages of mineral exploration is the main problem of data-driven mineral potential modeling methods. On the other hand, applying the expert’s knowledge and judgment in different stages of mineral potential modeling, is the main difficulty of knowledge-driven mineral potential modeling methods. In addition, other difficulties in these methods can be mentioned such as determination of important variables, weighting to various classes of maps or information layers, and so on. Hence, the accuracy of the results of the knowledge-driven modeling methods is highly dependent on the amount of knowledge and experience of the expert. In this study, the principal component analysis (PCA) has been introduced as a knowledge-driven method with the least reliance on the expert’s knowledge for mineral potential modeling. In this method, the expert’s knowledge is only used in the interpretation of the results obtained from the modeling, and is not considered in the first stages of mineral potential modeling and definition of the conceptual model. In the introduced method, the interpretation of the results is conducted based on the positive and negative coefficients of variables in the eigenvalues table. Using these coefficients, it is determined that each principal component (PC) is associated with what type of mineralization. An advantage of this introduced method is to identify various types of mineralization in the area of interest using just one modeling effort. In order to evaluate the efficiency of this method, a region including two geological maps of Kadkan and Shamkan in the south of Neyshabur, northeast of Iran was selected. Two mineralization types including podiform chromite and epithermal gold-antimony mineralization types have been identified using the proposed method that presents more precise results than those of conventional univariate and multivariate geochemical studies.
https://ijmge.ut.ac.ir/article_84225_720de2b0db162f89f1ca97884d90e34d.pdf
2021-12-01
161
170
10.22059/ijmge.2020.292411.594830
Mineral potential modeling
Principal component analysis
Podiform chromite deposit
Epithermal gold deposit
Kadkan and Shamkan area
Hamed
Fazliani
hamedfazliani@gmail.com
1
Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
LEAD_AUTHOR
Abolghasem
Kamkar-Rouhani
kamkar@shahroodut.ac.ir
2
Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
AUTHOR
Alireza
Arab-Amiri
alirezaarabamiri@yahoo.com
3
Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
AUTHOR
[1] Yang, J., Cheng, Q. (2015 a). A comparative study of independent component analysis with principal component analysis in geological objects identification, Part I: Simulations. Journal of Geochemical Exploration, 149, 127–135.
1
[2] Davis, J. C. (2002). Statistics and Data Analysis in Geology. 3rd ed, JohnWiley & SonsInc, New York.
2
[3] Cheng, Q., Bonham-Carter, G., Wang, W., Zhang, S., Li, W., Xia, Q. (2011). A spatially weighted principal component analysis for multi-element geochemical data for mapping locations of felsic intrusions in the Gejiu mineral district of Yunnan, China. Computers & Geosciences, 37 (5), 662–669.
3
[4] Wang, W., Zhao, J., Cheng, Q. (2011). Analysis and integration of geo-information to identify granitic intrusions as exploration targets in southeastern Yunnan District, China. Computers & Geosciences, 37, 1946–1957.
4
[5] Zuo, R. (2011). Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt, Tibet (China). Journal of Geochemical Exploration, 111, 13–22.
5
[6] Yang, J., Cheng, Q. (2015 b). A comparative study of independent component analysis with principal component analysis in geological objects identification, Part II: A case study of Pinghe District, Fujian, China, Journal of Geochemical Exploration, 149, 136–146.
6
[7] Ueki, K., and Iwamori, H. (2017). Geochemical differentiation processes for arc magma of the Sengan volcanic cluster, Northeastern Japan, constrained from the principal component analysis. Lithos, 290, 60–75.
7
[8] Davydenko, A. Y., Grayver, A. V. (2014). Principal component analysis for filtering and leveling of geophysical data. Journal of Applied Geophysics, 109, 266–280.
8
[9] Li, Q., Dehler, S. A. (2015). Inverse spatial principal component analysis for geophysical survey data interpolation. Journal of Applied Geophysics, 115, 79-91.
9
[10] Wang, J., Xiaohong, M., Li, F. (2015). Improved curvature gravity gradient tensor with principal component analysis and its application in edge detection of gravity data. Journal of Applied Geophysics, 118, 106–114.
10
[11] Zhang, Q., Peng, C., Lu, Y., Wang, H., Zhu, K. (2018). Airborne electromagnetic data levelling using principal component analysis based on flight line difference. Journal of Applied Geophysics, 151, 290–297.
11
[12] Ghosh, T., Basu S., Hazra S. (2014). Geological mapping of the Schuppen belt of north-east India using geospatial technology. Journal of Asian Earth Sciences, 79, 97–111.
12
[13] Mandeng, E. P. B., Bidjeck, L. M. B., Wambo, J. D. T., Taku, A., Betsi, T. B., Ipan, A. S., Nfada, L. T., Dieudonné, L. B. (2018). Lithologic and structural mapping of the Abiete–Toko gold district in southern Cameroon, using Landsat 7 ETM+/SRTM. Comptes Rendus Geoscience, 350, 130–140.
13
[14] Fazliani, H., Kamkar-Rouhani, A., Arab-Amiri, A. R. (2019). Fuzzy logic and principal component analysis for mineral potential modeling of epithermal gold-antimony deposits in southern Neyshabur. 11th Symposium of the Iranian Society of Economic Geology. Ahvaz, Iran.
14
[15] Ranjbar, H., Hassanzadeh, H., Torabi, M., Ilaghi, O. (2001). Integration and analysis of airborne geophysical data of the Darrehzar area, Kerman Province, Iran, using principal component analysis. Journal of applied geophysics , 48, 33–41.
15
[16] Honarmand, M., Ranjbar, H., Moezifar, Z. (2002). Integration and analysis of airborne geophysics and remote sensing data of sar-cheshmeh area, using directed principal component analysis. Exploration and Mining Geology, 11, 43–48.
16
[17] Ranjbar, H., Honarmand, M., Moezifar, Z. (2003b). Integration and analysis of airborne geophysics and remote sensing data for exploration of porphyry copper deposits in the Central Iranian Volcanic Belt. Map Asia Conference.
17
[18] Dezhong, H., Delian, L., Shuigen, X. (1995a). Explanatory text of geochemical map of Shamkan, stream sediment survey. Geological Survey of Iran Press
18
[19] Naderi Mighan, N. (1998). Geological map of Shamkan (1:100000). Geological Survey of Iran Press.
19
[20] Naderi Mighan, N. (1999). Geological map of Kadkan (1:100000) . Geological Survey of Iran Press.
20
[21] Carranza, E. J. M., (2008). Geochemical anomaly and mineral prospectivity mapping in GIS, handbook of exploration and environmental geochemistry. Vol. 11, Elsevier, Amsterdam.
21
[22] Rencher, A. C. (2002). Method of Multivariate Analysis . second Edition, Wiley series in probability and statistics.
22
[23] Sabins, F. F. (1999). Remote sensing for mineral exploration. Remote Sensing Enterprises, 1724 Celeste Lane, Fullerton, CA 92833, USA, 157–183.
23
[24] Ranjbar, H., Honarmand, M., Moezifar, Z. (2003a). Analysis of ETM+ and airborne geophysical data for exploration of porphyry type deposits in the Central Iranian Volcanic Belt, using fuzzy classification. Remote sensing for environmental monitoring, GIS applications, and geology III. SPIE Conference, Barcelona, Spain.
24
[25] Stern, R. J. (1999). Mineral exploration with satellite remote sensing imagery: examples from the Neoproterozoic Arabian-Nubian Shield. 11th International Conference of the Geological Society of Africa.
25
[26] Volesky, J. C., Stern, R. J., Johnson, P. R. (2003). Geological control of massive sulfide mineralization in the Neoproterozoic Wadi Bidah shear zone, southwestern Saudi Arabia, inferences from orbital remote sensing and field studies. Precambrian Research, 123, 235–247.
26
[27] Adler-Golden, S., Berk, A., Bernstein, L. S., Richtsmeier, S., Acharya, P. K., Matthew, M. W., Anderson, G. P., Allred, C., Jeong, L. Chetwynd, J. H. (1998). FLAASH, a MODTRAN4 atmospheric correction package for hyperspectral data retrievals and simulations. In Summaries of the seventh JPL airborne earth science workshop. 9-14.
27
[28] Costa, L., Nunes, L., Ampatzidis, Y. (2020). A new visible band index (vNDVI) for estimating NDVI values on RGB images utilizing genetic algorithms. Computers and Electronics in Agriculture, 172.
28
[29] Hardcastle, K. C. (1995). Photolineament Factor: A new computer-aided method for remotely sensing the degree to which bedrock is fractured. Photogrammetric Engineering & Remote Sensing, 61(6), 739– 747.
29
[30] Dezhong, H., Delian, L., Shuigen, X. (1995b). Explanatory text of geochemical map of Kadkan, stream sediment survey. Geological Survey of Iran Press.
30
[31] Beus, A. A., Gregorian, S. V. (1975). Geochemical exploration methods for mineral deposits. Applied Pub. Ud., Wilmette. [32] Filzmoser P., Hron K., Reimann C. (2009). Principal component analysis for compositional data with outliers. Environmetrics, 20, 621–632.
31
[33] Salomão, G. N., Figueiredo, M. A., Dall'Agnol, R., Sahoo, P. K., de Medeiros Filho, C. A., da Costa, M. F., Angélica, R. S. (2019). Geochemical mapping and background concentrations of iron and potentially toxic elements in active stream sediments from Carajás, Brazil – implication for risk assessment. Journal of South American Earth Sciences, 92, 151–166.
32
[34] Bishop, M. (1996). Neural Networks for Pattern Recognition. MIT Press.
33
[35] Theodoridis, S., Koutroumbas, K. (1999). Pattern Recognition, Academic Press.
34
[36] Dorri, M. B., Sadeghi, Kh. (2008). Regional exploration on the Kadkan 1:100000 geological map. Geological Survey of Iran Press.
35
[37] Heydari, E., Manaf Nejad, M. S. (2009). Regional exploration on the Shamkan 1:100000 geological map. Geological Survey of Iran Press.
36
[38] Azmi, H. (2011). Mineral prospecting over an area of 500 km2 in different locations of Khorasan Razavi province. Geological Survey of Iran Press.
37
[39] Mosier, D. L., Singer, D. A., Moring, B. C., Galloway, J. P. (2012). Podiform chromite deposits database and grade and tonnage models, U.S. Geological Survey Scientific Investigations Report 2012–5157, 45 p. and database.
38
[40] Constantinou, G. (1980). Metallogenesis associated with Troodos ophiolite. In Panayiotou, A. (Ed.), Ophiolites. Proceedings of the International Ophiolite Symposium, Nicosia, Cyprus, 663–674.
39
[41] Yaghubpur, A. and Hassan Nejad A. A. (2006). The spatial distribution of some chromite deposits in Iran, Using Fry Analysis. Journal of Sciences, Islamic Republic of Iran, 17(2), 147–152.
40
[42] Wells, F. G., Cater, F. W., Jr., Rynearson, G. A. (1946). Chromite deposits of Del Norte County, California. California Division of Mines Bulletin, 134, 1–76.
41
[43] Abrams, M. J., Rothery, D. A., Pontual, A. (1988). Mapping in the Oman Ophiolite using enhanced Landsat Thematic Mapper images. Tectonophysics, 151, 387–401.
42
[44] Lipin, B. R. (1984). Chromite from the Blue Ridge Province of North Carolina. American Journal of Science, 284, 507–529.
43
[45] Fazliani, H., Rahimi Pour, Gh. R., Ranjbar, H. (2007). Investigation of mineral zoning in the Kadkan and Shamkan geological sheets using the stream sediment geochemical data. 26th Geoscience Conference. Geological Survey of Iran, Tehran.
44
[46] Hedenquist, J. W., Izawa, E., Arribas, A., White, N. C. (1996). Epithermal Gold Deposits: Styles, Characteristics, and Exploration. Society of Resource Geology of Japan.
45
[47] Simmons, S. F., White, N. C., JOHN, D. A. (2005). Geological Characteristics of Epithermal Precious and Base Metal Deposits: in: Hedenquist, J. W., Thompson, J. F. H., Goldfarb, R. J., and Richards, J. P., eds., Economic Geology 100th Anniversary Volume: The Economic Geology Publishing Company, 485–522
46
[48] Taylor, B. E. (2007). Epithermal gold deposits . in Goodfellow, W. D., ed., Mineral Deposits of Canada: A Synthesis of Major Deposit-Types, District Metallogeny, the Evolution of Geological Provinces, and Exploration Methods: Geological Association of Canada, Mineral Deposits Division, Special Publication No. 5, 113–139.
47
[49] Sawkins, F. J. (1990). Metal deposits in relation to plate tectonics. Berlin, Springer-Verlag, 461.
48
[50] Sillitoe, R. H., Hedenquist, J. W. (2003). Linkages between volcano-tectonic settings, ore fluid compositions, and epithermal precious metal deposits . Society of Economic Geologists Special Publication 10, 315–343.
49
[51] Bethke, P. M., Rye, R. O., Stoffregen, R. E., Vikre, P. G. (2005). Evolution of the magmatic-hydrothermal acid-sulfate system at Summitville, Colorado: Integration of geological, stable isotope, and fluid inclusion evidence. Chemical Geology, 215, 281–315.
50
[52] Arancibia, G., Matthews, S. J., Cornejo, P., Perez de Arce, C., Zuluaga, J. I., and Kasanevan, S. (2006). 40Ar/ 39Ar and K-Ar geochronology of magmatic and hydrothermal events in a classic low-sulphidation epithermal bonanza deposit; El Peñon, Northern Chile. Mineralium Deposita , 41, 505–516.
51
[53] Harvey, B., Myers, S., Klein, T. (1999). Yanacocha gold district, northern Peru. Pacific Rim Congress , Bali, Indonesia, Australasian Institute of M
52
[1] Yang, J., Cheng, Q. (2015 a). A comparative study of independent component analysis with principal component analysis in geological objects identification, Part I: Simulations. Journal of Geochemical Exploration, 149, 127–135.
53
[2] Davis, J. C. (2002). Statistics and Data Analysis in Geology. 3rd ed, JohnWiley & SonsInc, New York.
54
[3] Cheng, Q., Bonham-Carter, G., Wang, W., Zhang, S., Li, W., Xia, Q. (2011). A spatially weighted principal component analysis for multi-element geochemical data for mapping locations of felsic intrusions in the Gejiu mineral district of Yunnan, China. Computers & Geosciences, 37 (5), 662–669.
55
[4] Wang, W., Zhao, J., Cheng, Q. (2011). Analysis and integration of geo-information to identify granitic intrusions as exploration targets in southeastern Yunnan District, China. Computers & Geosciences, 37, 1946–1957.
56
[5] Zuo, R. (2011). Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt, Tibet (China). Journal of Geochemical Exploration, 111, 13–22.
57
[6] Yang, J., Cheng, Q. (2015 b). A comparative study of independent component analysis with principal component analysis in geological objects identification, Part II: A case study of Pinghe District, Fujian, China, Journal of Geochemical Exploration, 149, 136–146.
58
[7] Ueki, K., and Iwamori, H. (2017). Geochemical differentiation processes for arc magma of the Sengan volcanic cluster, Northeastern Japan, constrained from the principal component analysis. Lithos, 290, 60–75.
59
[8] Davydenko, A. Y., Grayver, A. V. (2014). Principal component analysis for filtering and leveling of geophysical data. Journal of Applied Geophysics, 109, 266–280.
60
[9] Li, Q., Dehler, S. A. (2015). Inverse spatial principal component analysis for geophysical survey data interpolation. Journal of Applied Geophysics, 115, 79-91.
61
[10] Wang, J., Xiaohong, M., Li, F. (2015). Improved curvature gravity gradient tensor with principal component analysis and its application in edge detection of gravity data. Journal of Applied Geophysics, 118, 106–114.
62
[11] Zhang, Q., Peng, C., Lu, Y., Wang, H., Zhu, K. (2018). Airborne electromagnetic data levelling using principal component analysis based on flight line difference. Journal of Applied Geophysics, 151, 290–297.
63
[12] Ghosh, T., Basu S., Hazra S. (2014). Geological mapping of the Schuppen belt of north-east India using geospatial technology. Journal of Asian Earth Sciences, 79, 97–111.
64
[13] Mandeng, E. P. B., Bidjeck, L. M. B., Wambo, J. D. T., Taku, A., Betsi, T. B., Ipan, A. S., Nfada, L. T., Dieudonné, L. B. (2018). Lithologic and structural mapping of the Abiete–Toko gold district in southern Cameroon, using Landsat 7 ETM+/SRTM. Comptes Rendus Geoscience, 350, 130–140.
65
[14] Fazliani, H., Kamkar-Rouhani, A., Arab-Amiri, A. R. (2019). Fuzzy logic and principal component analysis for mineral potential modeling of epithermal gold-antimony deposits in southern Neyshabur. 11th Symposium of the Iranian Society of Economic Geology. Ahvaz, Iran.
66
[15] Ranjbar, H., Hassanzadeh, H., Torabi, M., Ilaghi, O. (2001). Integration and analysis of airborne geophysical data of the Darrehzar area, Kerman Province, Iran, using principal component analysis. Journal of applied geophysics , 48, 33–41.
67
[16] Honarmand, M., Ranjbar, H., Moezifar, Z. (2002). Integration and analysis of airborne geophysics and remote sensing data of sar-cheshmeh area, using directed principal component analysis. Exploration and Mining Geology, 11, 43–48.
68
[17] Ranjbar, H., Honarmand, M., Moezifar, Z. (2003b). Integration and analysis of airborne geophysics and remote sensing data for exploration of porphyry copper deposits in the Central Iranian Volcanic Belt. Map Asia Conference.
69
[18] Dezhong, H., Delian, L., Shuigen, X. (1995a). Explanatory text of geochemical map of Shamkan, stream sediment survey. Geological Survey of Iran Press
70
[19] Naderi Mighan, N. (1998). Geological map of Shamkan (1:100000). Geological Survey of Iran Press.
71
[20] Naderi Mighan, N. (1999). Geological map of Kadkan (1:100000) . Geological Survey of Iran Press.
72
[21] Carranza, E. J. M., (2008). Geochemical anomaly and mineral prospectivity mapping in GIS, handbook of exploration and environmental geochemistry. Vol. 11, Elsevier, Amsterdam.
73
[22] Rencher, A. C. (2002). Method of Multivariate Analysis . second Edition, Wiley series in probability and statistics.
74
[23] Sabins, F. F. (1999). Remote sensing for mineral exploration. Remote Sensing Enterprises, 1724 Celeste Lane, Fullerton, CA 92833, USA, 157–183.
75
[24] Ranjbar, H., Honarmand, M., Moezifar, Z. (2003a). Analysis of ETM+ and airborne geophysical data for exploration of porphyry type deposits in the Central Iranian Volcanic Belt, using fuzzy classification. Remote sensing for environmental monitoring, GIS applications, and geology III. SPIE Conference, Barcelona, Spain.
76
[25] Stern, R. J. (1999). Mineral exploration with satellite remote sensing imagery: examples from the Neoproterozoic Arabian-Nubian Shield. 11th International Conference of the Geological Society of Africa.
77
[26] Volesky, J. C., Stern, R. J., Johnson, P. R. (2003). Geological control of massive sulfide mineralization in the Neoproterozoic Wadi Bidah shear zone, southwestern Saudi Arabia, inferences from orbital remote sensing and field studies. Precambrian Research, 123, 235–247.
78
[27] Adler-Golden, S., Berk, A., Bernstein, L. S., Richtsmeier, S., Acharya, P. K., Matthew, M. W., Anderson, G. P., Allred, C., Jeong, L. Chetwynd, J. H. (1998). FLAASH, a MODTRAN4 atmospheric correction package for hyperspectral data retrievals and simulations. In Summaries of the seventh JPL airborne earth science workshop. 9-14.
79
[28] Costa, L., Nunes, L., Ampatzidis, Y. (2020). A new visible band index (vNDVI) for estimating NDVI values on RGB images utilizing genetic algorithms. Computers and Electronics in Agriculture, 172.
80
[29] Hardcastle, K. C. (1995). Photolineament Factor: A new computer-aided method for remotely sensing the degree to which bedrock is fractured. Photogrammetric Engineering & Remote Sensing, 61(6), 739– 747.
81
[30] Dezhong, H., Delian, L., Shuigen, X. (1995b). Explanatory text of geochemical map of Kadkan, stream sediment survey. Geological Survey of Iran Press.
82
[31] Beus, A. A., Gregorian, S. V. (1975). Geochemical exploration methods for mineral deposits. Applied Pub. Ud., Wilmette. [32] Filzmoser P., Hron K., Reimann C. (2009). Principal component analysis for compositional data with outliers. Environmetrics, 20, 621–632.
83
[33] Salomão, G. N., Figueiredo, M. A., Dall'Agnol, R., Sahoo, P. K., de Medeiros Filho, C. A., da Costa, M. F., Angélica, R. S. (2019). Geochemical mapping and background concentrations of iron and potentially toxic elements in active stream sediments from Carajás, Brazil – implication for risk assessment. Journal of South American Earth Sciences, 92, 151–166.
84
[34] Bishop, M. (1996). Neural Networks for Pattern Recognition. MIT Press.
85
[35] Theodoridis, S., Koutroumbas, K. (1999). Pattern Recognition, Academic Press.
86
[36] Dorri, M. B., Sadeghi, Kh. (2008). Regional exploration on the Kadkan 1:100000 geological map. Geological Survey of Iran Press.
87
[37] Heydari, E., Manaf Nejad, M. S. (2009). Regional exploration on the Shamkan 1:100000 geological map. Geological Survey of Iran Press.
88
[38] Azmi, H. (2011). Mineral prospecting over an area of 500 km2 in different locations of Khorasan Razavi province. Geological Survey of Iran Press.
89
[39] Mosier, D. L., Singer, D. A., Moring, B. C., Galloway, J. P. (2012). Podiform chromite deposits database and grade and tonnage models, U.S. Geological Survey Scientific Investigations Report 2012–5157, 45 p. and database.
90
[40] Constantinou, G. (1980). Metallogenesis associated with Troodos ophiolite. In Panayiotou, A. (Ed.), Ophiolites. Proceedings of the International Ophiolite Symposium, Nicosia, Cyprus, 663–674.
91
[41] Yaghubpur, A. and Hassan Nejad A. A. (2006). The spatial distribution of some chromite deposits in Iran, Using Fry Analysis. Journal of Sciences, Islamic Republic of Iran, 17(2), 147–152.
92
[42] Wells, F. G., Cater, F. W., Jr., Rynearson, G. A. (1946). Chromite deposits of Del Norte County, California. California Division of Mines Bulletin, 134, 1–76.
93
[43] Abrams, M. J., Rothery, D. A., Pontual, A. (1988). Mapping in the Oman Ophiolite using enhanced Landsat Thematic Mapper images. Tectonophysics, 151, 387–401.
94
[44] Lipin, B. R. (1984). Chromite from the Blue Ridge Province of North Carolina. American Journal of Science, 284, 507–529.
95
[45] Fazliani, H., Rahimi Pour, Gh. R., Ranjbar, H. (2007). Investigation of mineral zoning in the Kadkan and Shamkan geological sheets using the stream sediment geochemical data. 26th Geoscience Conference. Geological Survey of Iran, Tehran.
96
[46] Hedenquist, J. W., Izawa, E., Arribas, A., White, N. C. (1996). Epithermal Gold Deposits: Styles, Characteristics, and Exploration. Society of Resource Geology of Japan.
97
[47] Simmons, S. F., White, N. C., JOHN, D. A. (2005). Geological Characteristics of Epithermal Precious and Base Metal Deposits: in: Hedenquist, J. W., Thompson, J. F. H., Goldfarb, R. J., and Richards, J. P., eds., Economic Geology 100th Anniversary Volume: The Economic Geology Publishing Company, 485–522
98
[48] Taylor, B. E. (2007). Epithermal gold deposits . in Goodfellow, W. D., ed., Mineral Deposits of Canada: A Synthesis of Major Deposit-Types, District Metallogeny, the Evolution of Geological Provinces, and Exploration Methods: Geological Association of Canada, Mineral Deposits Division, Special Publication No. 5, 113–139.
99
[49] Sawkins, F. J. (1990). Metal deposits in relation to plate tectonics. Berlin, Springer-Verlag, 461.
100
[50] Sillitoe, R. H., Hedenquist, J. W. (2003). Linkages between volcano-tectonic settings, ore fluid compositions, and epithermal precious metal deposits . Society of Economic Geologists Special Publication 10, 315–343.
101
[51] Bethke, P. M., Rye, R. O., Stoffregen, R. E., Vikre, P. G. (2005). Evolution of the magmatic-hydrothermal acid-sulfate system at Summitville, Colorado: Integration of geological, stable isotope, and fluid inclusion evidence. Chemical Geology, 215, 281–315.
102
[52] Arancibia, G., Matthews, S. J., Cornejo, P., Perez de Arce, C., Zuluaga, J. I., and Kasanevan, S. (2006). 40Ar/ 39Ar and K-Ar geochronology of magmatic and hydrothermal events in a classic low-sulphidation epithermal bonanza deposit; El Peñon, Northern Chile. Mineralium Deposita , 41, 505–516.
103
[53] Harvey, B., Myers, S., Klein, T. (1999). Yanacocha gold district, northern Peru. Pacific Rim Congress , Bali, Indonesia, Australasian Institute of Mining and Metallurgy, Proceedings, 445–459.
104
ORIGINAL_ARTICLE
Geochemical potential mapping of iron-oxide targets by Prediction-Area plot and Concentration-Number fractal model in Esfordi, Iran
This study serves the purpose of generating a geochemical Fe-bearing potential map. Stream sediment geochemical survey was employed by collecting 843 samples for analyzing 19 elements and oxides. Taking preprocessing of data (e.g. outlier correction and data normalization) into consideration, a Concentration–Number (C-N) fractal model was used to separate different geochemical populations of Fe2O3, TiO2, V and the main multi-element factor in close spatially association with the Fe targeting. A prediction-area (P-A) plot was drawn for each variable to determine the weight of each geochemical indicator. Results indicate that the main geochemical factor with an ore prediction rate of 73%, has occupied 27% of the Esfordi area as favorable zones for further mining propsectivity. The Esfordi as a favorable Fe-bearing zone is of special interest in the NE of the Bafq mining district that hosts important “Kiruna-type” Magnetite-Apatite deposits. In addition, a synthesized indicators map was prepared through implementing a data-driven multi-class index overlay in a similar fashion to the previous version of the method, upon which geochemical potential zones were mostly in the NE part of the Esfordi, intimately linked with intense fault density map. The significance of this study lies in localizing of the most geochemical favorable zones through simultaneous consideration of the C-N and P-A plots accompanied with the incorporation of known active mines and prospects to determine indicator weight. Of note is that the Mineral Potential Mapping (MPM) has higher efficiency over each geochemical indicator with an ore prediction rate of 78% and area occupation of 22%.
https://ijmge.ut.ac.ir/article_84226_fcde206052a77adbf49f7c46420b07a4.pdf
2021-12-01
171
181
10.22059/ijmge.2021.309744.594863
Esfordi
Stream Sediment
C-N fractal model
P-A plot
Data-driven index overlay
Fardin
Ahmadi
f.ahmadi_uk@yahoo.com
1
Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
LEAD_AUTHOR
Hamid
Aghajani
haghajani@shahroodut.ac.ir
2
Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
AUTHOR
Maysam
Abedi
maysamabedi@ut.ac.ir
3
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
[1] Karimpour M. 1989. Applied Economic Geology. Javid Publication, Mashhad, Iran, 404 pp.
1
[2] Förster, H., and Jafarzadeh, A., (1994). The Bafq mining district in Central Iran - a highly mineralized Infracambrian volcanic field:
2
[3] Mazaheri S. A., Andrew A. S. &Chenhall B. E. (1994). Petrological studies of Sangan iron ore deposit. Center for isotope studies, Research Report, Sydney, Australia, pp. 48-52.
3
[4] Daliran F (2002) Kiruna-type iron oxide-apatite ores and apatitites of the Bafq district, Iran, with an emphasis on the REE geochemistry of their apatites. In: Porter, T.M. (Ed.), Hydrothermal Iron Oxide Copper-Gold and Related Deposits: A Global Perspective, vol. 2. PGC Publishing, Adelaide, pp. 303–320.
4
[5] Maanijou M. (2002). Proterozoic metallogeny of Iran. In: International Symposium of Metallogeny of Precambrian Shields, p. 2.13. Kyiv, Ukraine.
5
[6] Daliran F., Stosch H. G. & Williams P. (2007). Multistage metasomatism and mineralization at hydrothermal Fe oxide REE-apatite deposits and ‘apatitites’ of the Bafq district, centraleast Iran. In: Stanely C. J. eds. Digging Deeper, pp. 1501-1504. Proceedings 9th Biennial SGA Meeting Dublin, Ireland.
6
[7] Daliran F, Stosch HG, Williams P, Jamali H, Dorri MB (2010) Early Cambrian iron oxide-apatite-REE (U) deposits of the Bafq District, east-central Iran. Exploring for Iron oxide copper-gold deposits: Canada and Global analogues. Geol Assoc Canada, Short Course Notes 20: 143- 155.
7
[8] Jami M., Dunlop A. C. & Cohen D. R. (2007). Fluid inclusion and stable isotope study of the Esfordi apatite-magnetite deposit, Central Iran. Economic Geology 102, 1111-1128.
8
[9] Daliran, F., (1990). The magnetite-apatite deposit of Mishdovan, East Central Iran. An alkali rhyolite hosted, “Kiruna type” occurrence in the InfracambrianBafqmetallotect (mineralogic, petrographic and geochemical study of the ores and the host rocks): Ph.D. thesis, Heidelberg, Heidelberger GeowissenschaftlicheAbhandlungen 37, 248 p.
9
[10] Samani B. (1993). Saghand Formation, a riftogenic unit of upper Precambrian in Central Iran. Geosciences Scientific Quarterly Journal 6, 32-45. (In Persian with English abstract).
10
[11] Bonyadi Z., Davidson G. J.,Mehrabi B.,Meffre S. &Ghazban F. (2011). Significance of apatite REE depletion and monazite inclusions in the brecciated Se-Chahun iron oxide-apatite deposit, Bafq district, Iran: Insights from paragenesis and geochemistry. Chemical Geology 281, 253-269.
11
[12] Torab F., M. Lehmann B. (2006). Iron oxide-apatite deposits of the Bafq district, Central Iran: an overview from geology to mining. World of Mining—Surface and Underground 58, 355-362.
12
[13] Mohammad Torab, F., (2008). Geochemistry and metallogeny of magnetite apatite deposits of the Bafq Mining District, Central Iran, Doctoral Thesis, Faculty of Energy and the Economic Sciences Clausthal University of Technology.
13
[14] Sadeghi, B., Khalajmasoumi, M., Afzal, P., Moarefvand, P., Yasrebi, A. B., Wetherelt, A., &Ziazarifi, A. (2013). Using ETM+ and ASTER sensors to identify iron occurrences in the Esfordi 1: 100,000 mapping sheet of Central Iran. Journal of African Earth Sciences, 85, 103-114.
14
[15] Samani, B.A., (1988).Metallogeny of the Precambrian in Iran: Precambrian Research, v. 39, p. 85-106.
15
[16] Mücke, A., Younessi, R., (1994). Magnetite-apatite deposits (Kiruna-type) along the Sanandaj-Sirjan zone and in the Bafq area, Iran, associated with ultramafic and calc-alkaline rocks and carbonatites: Mineralogy and Petrology, v. 50, p. 219-244.
16
[17] Harris J, Wilkinson L, Grunsky E, Heather K, Ayer J (1999). Techniques for analysis and visualization of lithogeochemical data with applications to the Swayze greenstone belt, Ontario, Journal of Geochemical Exploration 67:301-334.
17
[18] Cheng Q (1999). Spatial and scaling modelling for geochemical anomaly separation, Journal of Geochemical exploration 65:175-194.
18
[19] Cheng, Q., Agterberg, F. and Bonham-Carter, G., (1996) A spatial analysis method for geochemical anomaly separation. Journal of Geochemical Exploration, 56: 183-195.
19
[20] Ghavami-Riabi, R., Seyedrahimi-Niaraq, M., Khalokakaie, R. and Hazareh, M., (2010) U-spatial statistic data modeled on a probability diagram for investigation of mineralization phases and exploration of shear zone gold deposits. Journal of Geochemical Exploration, 104: 27-33.
20
[21] Darabi-Golestan, F., Ghavami-Riabi, R., Khalokakaie, R., Asadi-Haroni, H., Seyedrahimi-Niaraq, M., 2013, Interpretation of lithogeochemical and geophysical data to identify the buried mineralized area in Cu-Au porphyry of Dalli-Northern Hill. Arabian Journal of Geosciences, 6:4499-4509.
21
[22] Seyedrahimi-Niaraq, M., Hekmatnejad, A., (2020). The efficiency and accuracy of probability diagram, spatial statistic and fractal methods in the identification of shear zone gold mineralization: a case study of the Saqqez gold ore district, NW Iran. ActaGeochimica, https://doi.org/10.1007/s11631-020-004137.
22
[23] Qiuming C (2000).Multifractal theory and geochemical element distribution pattern, Earth Science-Journal of China University of Geosciences 25:311-318.
23
[24] Hawkes RAW, Webb HE (1979). Geochemistry in mineral exploration,2nd edn. Academic Press, New York, 657 pp.
24
[25] Li, C.J., Ma, T.H., Shi, J.F., (2003). Application of a fractal method relating concentration and distances for separation of geochemical anomalies from background. J GeochemExplor 77: 167–175.
25
[26] Mandelbrot, B.B., (1983). The Fractal Geometry of Nature. WH Freeman, San Francisco, pp 1-468.
26
[27] Cheng, Q., Agterberg, F.P., Ballantyne, S.B., (1994). The separation of geochemical anomalies from background by fractal methods. J GeochemExplor 51: 109–130.
27
[28] Afzal, P., FadakarAlghalandis, Y., Khakzad, A., Moarefvand, P., RashidnejadOmran, N., (2011). Delineation of mineralization zones in porphyry Cu deposits by fractal concentration–volume modeling. J GeochemExplor 108: 220–232.
28
[29] Afzal, P., DadashzadehAhari, H., RashidnejadOmran, N., Aliyari, F., (2013). Delineation of gold mineralized zones using concentration-volume fractal model in Qolqoleh gold deposit, NW Iran. Ore Geology Reviews 55: 125-133.
29
[30] Delavar, S.T., Afzal, P., Borg, G., Rasa, I., Lotfi, M., RashidnejadOmran, N., (2012). Delineation of mineralization zones using concentration-volume fractal method in Pb-Zn carbonate hosted deposits. J. Geochem. Explor. 118, 98–110.
30
[31] Zuo, R., (2011). Decomposing of mixed pattern of arsenic using fractal model in Gangdese belt, Tibet, China. Appl. Geochem. 26, S271–S273.
31
[32] Zuo, R., Wang, J., (2016). Fractal/multifractal modeling of geochemical data: a review. J. Geochem. Explor. 164, 33–41.
32
[33] Panahi, A., Cheng, Q., & Bonham-Carter, G. F. (2004). Modelling component, indicator kriging, and multifractal power-spectrum analysis: a case study from Gowganda, Ontario. Geochemistry: Exploration, Environment, Analysis, 4(1), 59-70.
33
[34] Mirzaie, M., Afzal, P., Adib, A., Rahimi, E., &Mohammadi, G. (2020). Detection of zones based on ore and gangue using fractal and multivariate analysis in ChahGaz iron ore deposit, Central Iran. Journal of Mining and Environment, 11(2), 453- 466.
34
[35] Nyka¨nen, V., Lahti, I., Niiranen, T., &Korhonen, K. (2015). Receiver operating characteristics (ROC) as validation tool for prospectivity models—a magmatic Ni–Cu case study from the Central Lapland Greenstone Belt, Northern Finland. Ore Geology Reviews, 71, 853–860.
35
[36] Yousefi, M., & Carranza, E. J. M. (2015). Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Computers &Geosciences, 79, 69-81.
36
[37] Carranza, E. J. M., &Laborte, A. G. (2016). Data-driven predictive modeling of mineral prospectivity using random forests: A case study in Catanduanes Island (Philippines). Natural Resources Research, 25, 35–50.
37
[38] Bonham-Carter, G. F., Agterberg, F. P., & Wright, D. F. (1989). Weights of evidence modelling: A new approach to mapping mineral potential. Statistical Applications in the Earth Sciences, 89, 171–183.nces, 85, 103-114.
38
[39] Yousefi, M., & Carranza, E. J. M. (2016). Data-driven index overlay and Boolean logic mineral prospectivity modeling in greenfields exploration. Natural Resources Research, 25, 3–18.
39
[40] Du, X., Zhou, K., Cui, Y., Wang, J., Zhang, N., & Sun, W. (2016). Application of fuzzy Analytical Hierarchy Process (AHP) and Prediction-Area (P-A) plot for mineral prospectivity mapping: A case study from the Dananhumetallogenic belt, Xinjiang, NW China. Arabian Journal of Geosciences, 9, 298.
40
[41] Gao, Y., Zhang, Z., Xiong, Y., &Zuo, R. (2016). Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China. Ore Geology Reviews, 75: 16–28.
41
[42] Nezhad, S. G., Mokhtari, A. R., &Rodsari, P. R. (2017). The true sample catchment basin approach in the analysis of stream sediment geochemical data. Ore Geology Reviews, 83, 127–134.
42
[43] Zhang, N., Zhou, K., & Du, X. (2017). Application of fuzzy logic and fuzzy AHP to mineral prospectivity mapping of porphyry and hydrothermal vein copper deposits in the Dananhu-Tousuquan island arc, Xinjiang, NW China. Journal of African Earth Sciences, 128, 84–96.
43
[44] Almasi, A., Yousefi, M., & Carranza, E. J. M. (2017). Prospectivityanalysis of orogenic gold deposits in Saqez-Sardasht Goldfield, Zagros Orogen, Iran. Ore Geology Reviews, 91, 1066–1080.
44
[45] Roshanravan, B., Aghajani, H., Yousefi, M., &Kreuzer, O. (2019). An improved prediction-area plot for prospectivity analysis of mineral deposits. Natural Resources Research, 28(3), 1089-1105.
45
[46] Bishop, C. M. (2006). Pattern recognition and machine learning. springer. [47] Coolbaugh, M.F., Raines, G.L., Zehner, R.E., (2007).Assessment of exploration bias in data-driven predictive models and the estimation of undiscovered resources. Nat. Resourc.Res.16, 199–207.
46
[48] Gansser A. (1981). The Geodynamic History of the Himalaya, in Zagros, Hindu Kush. In: Gupta H. K. & Delany F. M. eds. Himalaya-Geodynamik Evolution, Geodynamik Series, 3, pp. 111-121. American Geophysical Union, Washington DC. [49] Stocklin J. (1971). Stratigraphic Lexicon of Iran; Part 1: Tehran, Geological Survey of Iran, 338 p. Tehran Iran.
47
[50] Alavi M. (1991). Sedimentary and structural characteristics of the Paleo-Tethys remnants in northeastern Iran. Geological Society of America Bulletin 103, 983-992.
48
[51] Daliran F, Stosch HG, Williams PJ (2009). A review of the Early Cambrian magmatic and metasomatic events and their bearing on the genesis of the Fe oxide-REE-apatite deposits (IOA) of the Bafq district, Iran. In: Williams P (Ed.): Smart Science for Exploration and Mining. 10th SGA Biennial, Townsville, 623– 625.
49
[52] Gandhi SS (2003). An overview of the Fe oxide–Cu–Au deposits and related deposit types. CIM Montréal 2003 Mining Industry Conference and Exhibition, Canadian Institute of Mining, Technical Paper, CD-ROM.
50
[53] Williams PJ (2010). Classifying IOCG deposits. In: Corriveau L, Mumin H (Eds) Exploring for iron-oxide copper-gold deposits: Canada and Global Analogues: Geological Association. Canada short course notes 20: 11–19.
51
[54] Groves DI, Bierlein FP, Meinert LD, Hitzman MW (2010). Iron oxide copper-gold (IOCG) deposits through earth history: implications for origin, lithospheric setting, and distinction from other epigenetic iron oxide deposits. Economic Geology 105: 641-654.
52
[55] Ghorbani, M. (2013). Economic geology of Iran (Vol. 581). Berlin: Springer.
53
[56] Stosch HG, Romer RL, Daliran F, Rhede D (2011). Uranium-lead ages of apatite from iron oxide ores of the Bafq District, East-Central Iran. Miner Deposita46: 9–21.
54
[57] Nisco-National Iranian Steel Company (1980). Report on results of search and evaluation works at magnetic anomalies of the Bafq iron ore region during 1976-1979. Unpublished Report, p 260.
55
[58] Berberian M., & King G. C. P. (1981). Towards the Paleogeography and tectonic evolution of Iran. Canadian Journal of Earth Sciences 18, 210-265.
56
[59] Nabatian, G., Rastad, E., Neubauer, F., Honarmand, M., &Ghaderi, M. (2015). Iron and Fe–Mnmineralisation in Iran: implications for Tethyanmetallogeny. Australian Journal of Earth Sciences, 62(2), 211-241.
57
[60] Olea, R. A. (2006). A six-step practical approach to semivariogram modeling. Stochastic Environmental Research and Risk Assessment, 20(5), 307-318.
58
[61] Tahernejad, M. M., KhaloKakaei, R., &Ataei, M. (2018). Analyzing the effect of ore grade uncertainty in open pit mine planning; A case study of Rezvan iron mine, Iran. International Journal of Mining and Geo-Engineering, 52(1), 53-60.
59
[62] Riemann, C., Filzmoser, P., & Garrett, R. G. (2002). Factor analysis applied to regional geochemical data: problems and possibilities. Applied Geochemistry, 17(3), 185-206.
60
[63] Nazarpour, A., Omran, N. R., &Paydar, G. R. (2015). Application of multifractal models to identify geochemical anomalies in Zarshuran Au deposit, NW Iran. Arabian Journal of Geosciences, 8(2), 877-889.
61
[64] Khalifani, F., Bahroudi, A., Barak, S., &Abedi, M. (2019). An integrated Fuzzy AHP-VIKOR method for gold potential mapping in Saqez prospecting zone, Iran. Earth Observation and Geomatics Engineering, 3(1), 21-33.
62
[65] Grant, A. (1990). Multivariate statistical analyses of sediment geochemistry. Marine Pollution Bulletin, 21(6), 297-299. [66] Zumlot, A. B. T. (2012). Multivariate statistical approach to geochemical methods in water quality factor identification; application to the shallow aquifer system of the Yarmouk Basin of north Jordan. Research Journal of Environmental and Earth Sciences, 4(7), 756-768.
63
[67] Ammar, F. H., Chkir, N., Zouari, K., Hamelin, B., Deschamps, P., &Aigoun, A. (2014). Hydro-geochemical processes in the Complexe Terminal aquifer of southern Tunisia: An integrated investigation based on geochemical and multivariate statistical methods. Journal of African Earth Sciences, 100, 81-95.
64
[68] Karar, K., Gupta, A. K., Kumar, A., & Biswas, A. K. (2006). Characterization and identification of the sources of chromium, zinc, lead, cadmium, nickel, manganese, and iron in PM 10 particulates at the two sites of Kolkata, India. Environmental Monitoring and Assessment, 120(1-3), 347-360.
65
[69] Sprovieri, R., Thunell, R., & Howe, M. (2020). Paleontological and geochemical analysis of three laminated sedimentary units of late Pliocene-early Pleistocene age from the Monte San Nicola section in Sicily. RivistaItaliana di Paleontologia e Stratigrafia, 92(3).
66
[70] Hirst, D. M. (1974). Geochemistry of Sediments from Eleven Black Sea Cores: Geochemistry.
67
[71] Malinowski, E. R., &Howery, D. G. (1980). Factor analysis in chemistry (p. 10). New York: Wiley.
68
[72] Wu, R., Chen, J., Zhao, J., Chen, J., & Chen, S. (2020). Identifying Geochemical Anomalies Associated with Gold Mineralization Using Factor Analysis and Spectrum–Area Multifractal Model in Laowan District, Qinling-DabieMetallogenic Belt, Central China. Minerals, 10(3), 229.
69
[73] Goncalves MA, Vairinho M, Oliveira V (1998). Study of geochemical anomalies in Mombeja area using a multifractal methodology and geostatistics. In: Buccianti A, Nardi G, Potenza R (eds) IV IAMG'98. De Frede, Ischia Island, Italy, pp 590–595.
70
[74] Afzal, P., Zia Zarifi, A., &Sadeghi, B. (2013). Separation of Geochemical Anomalies Using Factor Analysis and Concentration-Number (CN) Fractal Modeling Based on Stream Sediments Data in Esfordi 1: 100000 Sheet, Central Iran. Iranian Journal of Earth Sciences, 5(2), 100-110.
71
[75] Momeni, S., Shahrokhi, S. V., Afzal, P., Sadeghi, B., Farhadinejad, T., &Nikzad, M. R. (2016). Delineation of the Cr mineralization based on the stream sediment data utilizing fractal modeling and factor analysis in the Khoy 1: 100,000 sheet, NW Iran. MadenTetkikveAramaDergisi, (152), 143-151.
72
[76] Asfahani, J. (2017). Fractal theory modeling for interpreting nuclear and electrical well logging data and establishing lithological cross-section in basaltic environment (case study from southern Syria). Applied Radiation and Isotopes, 123, 26-
73
ORIGINAL_ARTICLE
Improving the classification of facies quality in tight sands by petrophysical logs
As conventional hydrocarbon reserves are running out, attention is now being paid to unconventional hydrocarbon resources and reserves such as tight sands and hydrocarbon shales for future energy supplies. To achieve this, the identification of tight sand facies is based on zones containing mature hydrocarbons in priority. Organic geochemical methods are the commonest methods to evaluate the quality of these reservoirs. In this study, using a deep learning approach and using petrophysical logs, a suitable classification model for facies quality is presented. Moreover, the proposed method has been compared with two common methods: multilinear regression and multilayer perceptron neural network. The results indicated that the accuracy of facies classification using these three methods is about 63%, 71%, and 84% for linear multilinear regression, perceptron multilayer neural network and, deep learning, respectively. Finally, the accuracy of the deep learning networks was optimized using two gravitational search and whale optimization algorithms. It has been shown that the accuracy of deep learning was increased from 84% to 87% and 90.5% using the gravitational search algorithms and whale algorithms, respectively.
https://ijmge.ut.ac.ir/article_84227_64fa3485f610f4a4fba3f31801f04b67.pdf
2021-12-01
183
190
10.22059/ijmge.2021.313898.594878
Facies quality
Deep learning
Optimization Algorithm
tight sands
Classification
Yousef
Asgari Nezhad
yousefasgari@ut.ac.ir
1
School of Mining, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Ali
Moradzadeh
a_moradzadeh@ut.ac.ir
2
School of Mining, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
[1] Ding, J., Xiaozhi, C., Xiudi, J., Bin, W., & Jinmiao, Z. (2015).Application of AVF inversion on shale gas reservoir TOC prediction. Paper presented at the 2015 SEG Annual Meeting.
1
[2]Passey, Q., Creaney, S., Kulla, J., Moretti, F., & Stroud, J. (1990). A practical model for organic richness from porosity and resistivity logs. AAPG Bulletin, 74(12), 1777-1794.
2
[3] Lecompte, B., & Hursan, G. (2010). Quantifying source rock maturity from logs: how to get more than TOC from Delta Log R. Paper presented at the SPE Annual Technical Conference and Exhibition.
3
[4] Euzen, T., Power, M., Crombez, V., Rohais, S., Petrovic, M., & Carpentier, B. (2014). Lithofacies, Organic Carbon and Petrophysical Evaluation of the Montney and Doig Formations (Western Canada): Contribution of Quantitative Cuttings Analysis and Electrofacies Classification. Paper presented at the CSPG CSEG CWLS Joint Annual Convention, Calgary.
4
[5] Yuan, X., Lin, S., Liu, Q., Yao, J., Wang, L., Guo, H., et al. (2015). Lacustrine fine-grained sedimentary features and organic-rich shale distribution pattern: A case study of Chang 7 Member of Triassic Yanchang Formation in Ordos Basin, NW China. Petroleum Exploration and Development, 42(1), 34-43.
5
[6] Wang, P., Chen, Z., Pang, X., Hu, K., Sun, M., & Chen, X. (2016). Revised models for determining TOC in shale play: Example from Devonian Duvernay shale, Western Canada sedimentary basin. Marine and Petroleum Geology, 70, 304-319.
6
[7] Zhao, P., Mao, Z., Huang, Z., & Zhang, C. (2016). A new method for estimating total organic carbon content from well logs. AAPG Bulletin, 100(8), 1311-1327.
7
[8] Zhao, P., Ma, H., Rasouli, V., Liu, W., Cai, J., & Huang, Z. (2017). An improved model for estimating the TOC in shale formations. Marine and Petroleum Geology, 83, 174-183.
8
[9] Nie, X., Wan, Y., & Bie, F. (2017). Dual-shale-content method for total organic carbon content evaluation from wireline logs in organic shale. Open Geosciences, 9(1), 133-137.
9
[10] Huang, Z., & Williamson, M. A. (1996). Artificial neural network modelling as an aid to source rock characterization. Marine and Petroleum Geology, 13(2), 277-290.
10
[11] Kamali, M. R., & Mirshady, A. A. (2004). Total organic carbon content determined from well logs using ΔLogR and Neuro-Fuzzy techniques. Journal of Petroleum Science and Engineering, 45(3-4), 141-148.
11
[12] Kadkhodaie-Ilkhchi, A., Rahimpour-Bonab, H., & Rezaee, M. (2009). A committee machine with intelligent systems for estimation of total organic carbon content from petrophysical data: An example from Kangan and Dalan reservoirs in South Pars Gas Field, Iran. Computers & Geosciences, 35(3), 459-474.
12
[13] Sfidari, E., Kadkhodaie-Ilkhchi, A., & Najjari, S. (2012). Comparison of intelligent and statistical clustering approaches to predicting total organic carbon using intelligent systems. Journal of Petroleum Science and Engineering, 86, 190-205.
13
[14] Alizadeh, B., Najjari, S., & Kadkhodaie-Ilkhchi, A. (2012). Artificial neural network modeling and cluster analysis for organic facies and burial history estimation using well log data:A case study of the South Pars Gas Field, Persian Gulf, Iran. Computers & Geosciences, 45, 261-269.
14
[15] Alizadeh, B., Maroufi, K., & Heidarifard, M. H. (2018). Estimating source rock parameters using wireline data: An example from Dezful Embayment, South West of Iran. Journal of Petroleum Science and Engineering, 167, 857-868 .
15
[16] Khoshnoodkia, M., Mohseni, H., Rahmani, O., & Aali, J. (2010). Toc Determination of Gadvan Formation in South Pars Gas Field, Using Artificial Neural Network Technique. Paper presented at the GEO 2010.
16
[17] Mahmoud, A. A. A., Elkatatny, S., Mahmoud, M., Abouelresh, M., Abdulraheem, A., & Ali, A. (2017). Determination of the total organic carbon (TOC) based on conventional well logs using artificial neural network. International Journal of Coal Geology, 179, 72-80 .
17
[18] Zhu, L., Zhang, C., Zhang, C., Wei, Y., Zhou, X., Cheng, Y., et al. (2018). Prediction of total organic carbon content in shale reservoir based on a new integrated hybrid neural network and conventional well logging curves. Journal of Geophysics and Engineering, 15(3), 1050-1061.
18
[19] Tan, M., Liu, Q., & Zhang, S. (2013). A dynamic adaptive radial basis function approach for total organic carbon content prediction in organic shale. Geophysics, 78(6), D445-D459 .
19
[20] Amiri Bakhtiar, H., Telmadarreie, A., Shayesteh, M., Heidari Fard, M., Talebi, H., & Shirband, Z. (2011). Estimating total organic carbon content and source rock evaluation, applying ΔlogR and neural network methods: Ahwaz and Marun oilfields, SW of Iran. Petroleum Science and Technology, 29(16), 1691-1704.
20
[21] Wang, P., Peng, S., & He, T. (2018). A novel approach to total organic carbon content prediction in shale gas reservoirs with well logs data, Tonghua Basin, China. Journal of Natural Gas Science and Engineering, 55, 1-15 .
21
[22] Rui, J., Zhang, H., Zhang, D., Han, F., & Guo, Q. (2019). Total organic carbon content prediction based on a support-vectorregression machine with particle swarm optimization. Journal of Petroleum Science and Engineering.
22
[23] Asgari Nezhad, Y., Moradzadeh, A., & Kamali, M. R. (2018). A new approach to evaluate Organic Geochemistry Parameters by geostatistical methods: A case study from western Australia. Journal of Petroleum Science and Engineering, 169, 813-824 .
23
[24] An, P., & Cao, D. (2018). Shale content prediction based on LSTM recurrent neural network. Paper presented at the SEG 2018 Workshop: SEG Maximizing Asset Value Through Artificial Intelligence and Machine Learning, Beijing, China, 17-19 September 2018.
24
[25] Zhu, L., Zhang, C., Zhang, C., Zhang, Z., Nie, X., Zhou, X., et al. (2019). Forming a new small sample deep learning model to predict total organic carbon content by combining unsupervised learning with semisupervised learning. Applied Soft Computing, 83, 105596.
25
[26] Zhu, L., Zhang, C., Zhang, C., Zhang, Z., Zhou, X., Liu, W., et al. (2020). A new and reliable dual model-and data-driven TOC prediction concept: A TOC logging evaluation method using multiple overlapping methods integrated with semi-supervised deep learning. Journal of Petroleum Science and Engineering, 106944.
26
[27] Wang, K., Pang, X., Zhang, H., Hu, T., Xu, T., Zheng, T., et al. (2019). Organic geochemical and petrophysical characteristics of saline lacustrine shale in the Dongpu Depression, Bohai Bay Basin, China: Implications for Es3 hydrocarbon exploration. Journal of Petroleum Science and Engineering, 106546 .
27
[28] Mobil Oil Australia, 1983. Well completion report White Hills-1, Exploration Permit 134, Canning Basin, Western Australia. Geological Survey of Western Australia, S2086A2.
28
[29] Apak, S.N. and Carlsen, G.M., (1996). A Compilation and Review of Data Pertaining to the Hydrocarbon Prospectivity in the Canning Basin: Geological Survey of Westen Australia, Record 1996/10
29
[30] Weisberg, S. (2005). Applied linear regression (Vol. 528): John Wiley & Sons.
30
[31] Lashin, A., & El Din, S. S. (2013). Reservoir parameters determination using artificial neural networks: Ras Fanar field, Gulf of Suez, Egypt. Arabian Journal of Geosciences, 6(8), 2789-2806.
31
[32] Kainthola, A., Singh, P., Verma, D., Singh, R., Sarkar, K., & Singh, T. (2015). Prediction of strength parameters of Himalayan rocks: a statistical and ANFIS approach. Geotechnical and Geological Engineering, 33(5), 1255-1278.
32
[33] Kacprzyk, J. (2008). Studies in Computational Intelligence, Volume 100.
33
[34] Funahashi, K.-i., & Nakamura, Y. (1993). Approximation of dynamical systems by continuous-time recurrent neural networks. Neural networks, 6(6), 801-806.
34
[35] Hermans, M., & Schrauwen, B. (2013). Training and analyzing deep recurrent neural networks. Paper presented at the Advances in neural information processing systems.
35
[36] Rashedi, E., Nezamabadi-Pour, H. and Saryazdi, S., (2009). GSA: a gravitational search algorithm. Information sciences, 179(13), pp.2232-2248.
36
[37] Pelusi, D., Mascella, R., Tallini, L., Nayak, J., Naik, B., & Deng, Y. (2019). Improving exploration and exploitation via a Hyperbolic Gravitational Search Algorithm. Knowledge-Based Systems, 105404.
37
[38] Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67.
38
[39] Watkins, W. A., & Schevill, W. E. (1979). Aerial observation of feeding behavior in four baleen whales: Eubalaena glacialis, Balaenoptera borealis, Megaptera novaeangliae, and Balaenoptera physalus. Journal of Mammalogy, 60(1), 155-163.
39
[40] Tissot, B. P., & Welte, D. H. (2013). Petroleum formation and occurrence: Springer Science & Business Media.
40
[41] Suárez-Ruiz, I., Flores, D., Mendonça Filho, J. G., & Hackley, P. C. (2012). Review and update of the applications of organic petrology: Part 1, geological applications. International Journal of Coal Geology, 99, 54-112.
41
ORIGINAL_ARTICLE
The effect of inorganic acids on reducing iron impurities during iron-rich laterite ore leaching
The Recovery of nickel from lateritic ores as the main oxide resources has been always debated. Since it consists of 1.74% Ni, 0.14% Co and 40.8% Fe, co-dissolution of iron occurred by using common lixiviation like sulfuric acid. Therefore, some leaching agents should be sought due to promoting a high dissolution of nickel/cobalt and a negligible iron recovery. This research investigates the effect of using organic acids such as gluconic, lactic and citric acid along with sulfuric acid on recoveries of Ni/Co from an iron-rich laterite ore. The results showed that adding sulfuric acid to the optimal combined ratio of the organic acids (gluconic: lactic: citric= 1: 2: 3) to obtain the combined ratio of 6 : 1: 2: 3 (sulfuric: gluconic: lactic: citric acid), simultaneously increasing the temperature from 60 to 90 °C, and increasing the final combined concentration of the acids from 3.5 M to 5 M, significantly increased nickel and cobalt recoveries by 80.4 and 68.7%, respectively, and slightly increased iron extraction by 5.05% all when compared to using the optimal combined ratio of organic acids. The use of 5 M sulfuric acid alone as a leaching agent, at 90 ° C, resulted in an 81.11% increase in iron dissolution than the 6: 1: 2: 3 combination. The results obtained indicated that the reaction rate was controlled by the chemical reaction, and the activation energies of 42.71 kJ/mol for nickel and 84.57 kJ/mol for cobalt were consistent with this conclusion.
https://ijmge.ut.ac.ir/article_84228_fe13050a066f8ab316012e4fdb54ddc2.pdf
2021-12-01
191
199
10.22059/ijmge.2021.311273.594869
Laterite
nickel
Cobalt
Organic acids
Atmospheric leaching
Marzieh
Hosseini Nasab
hosseininasab@eng.usb.ac.ir
1
Department of Mining Engineering, University of Sistan and Baluchestan, Zahedan, Iran
LEAD_AUTHOR
Mohammad
Noaparast
noparast@ut.ac.ir
2
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Hadi
Abdollahi
h_abdollahi@ut.ac.ir
3
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
[1] Pawlowska, A., and Sadowski, Z. (2017). Influence of chemical and biogenic leaching on surface area and particle size of laterite ore. Physicochem. Probl. Miner. Process 53, 869-877.
1
[2] Lv, X., Lv, W., You, Z., Lv, X., and Bai, Ch. )2018). Non-isothermal kinetics study on carbothermic reduction of nickel laterite ore. Powder Technol. 340, 495–501.
2
[3] 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 Ser Mater Sci Eng 285, 1-5.
3
[4] Buyukakinci, E. (2008). Extraction of nickel from lateritic ores. Yüksek Lisans Tezi, Orta Doğu Teknik Üniversitesi.
4
[5] Li, G.H., Rao, M.J., Peng, Z.W., and Jiang, T. (2010). Extraction of cobalt from laterite ores by citric acid in presence of ammonium bifluoride, Trans. Nonferrous Met. Soc. China 20, 1517-1520.
5
[6] 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 Microbiol. Rev. 11, 87-95.
6
[7] Tang, J., and Valix, M. (2004). Leaching of low-grade nickel ores by fungi metabolic acids. In: Proceedings of Separations Technology VI: New Perspectives on Very Large-Scale Operations, 1-16.
7
[8] Simate, G.S., Ndlovu, S., and Walubita, L.F. (2010). The fungal and chemolithotrophic leaching of nickel laterites—Challenges and opportunities. HYDROMETALLURGY. 103, 150-157.
8
[9] Astuti, W., Hirajima, T., Sasaki, K., and Okibe, N. (2016). Comparison of effectiveness of citric acid and other acids in leaching of low-grade Indonesian saprolitic ores, Miner. Eng. 85, 1-16.
9
[10] Biswas, S., Chakraborty, S., Chaudhuri, M.G., Banerjee, P.C., Mukherjee, S., and Dey, R. (2014). Optimization of process parameters and dissolution kinetics of nickel and cobalt from lateritic chromite overburden using organic acids, J Chem Technol Biotechnol 89, 1491–1500.
10
[11] Javanshir, S., Mofrad, Z.H., Azargoon, A. (2018). Atmospheric pressure leaching of nickel from a low-grade nickel-bearing ore. Physicochem. Probl. Miner. Process 54(3), 890-900.
11
[12] D. B. Johnson, Reductive dissolution of minerals and selective recovery of metals using acidophilic iron- and sulfate-reducing acidophiles. Hydrometallurgy127-128 (2012) 172–177.
12
[13] Hosseini Nasab, M., Noaparast, M., and Abdollahi, H. (2020). Dissolution optimization and kinetics of nickel and cobalt from iron-rich laterite ore, using sulfuric acid at atmospheric pressure, Int J Chem Kinet. 52, 283–298.
13
[14] Hosseini Nasab, M., Noaparast, M., and Abdollahi, H. (2020). Dissolution of nickel and cobalt from iron-rich laterite ores using different organic acids, JME, doi:10.22044/jme.2020.9564.1869
14
[15] Miettinen, V., Mäkinen, J., Kolehmainen, E., Kravtsov, T., Rintala, L., (2019). Iron Control in Atmospheric Acid Laterite Leaching, Minerals 9, 404; doi:10.3390/min9070404
15
[16] Basturkcu, H., Acarkan, N., (2017). Selective nickel-iron separation from atmospheric leach liquor of a lateritic nickel ore using the para-goethite method, Physicochemical Problems of Mineral Processing 53(1): 212−226; doi:10.5277/ppmp170118
16
[17] Wang, K., Li, J., McDonald, R.G., Browner, R.E., (2018). Iron, aluminum, and chromium co-removal from atmospheric nickel laterite leach solutions, Minerals Engineering 116, 35–45; doi:10.1016/j.mineng.2017.10.019
17
[18] Astuti, W. (2015). Atmospheric leaching of Nickel from lowgrade Indonesian saprolite ores by biogenic citric acid, As partial fulfillment of the requirements for the degree of Doctor of Engineering, Kyushu University Fukuoka, Japan.
18
[19] Li, J., Li, X., Hu, Q., Wang, Z., Zhou, Y., Zheng, J., Liu, W., and Li, L. (2009). Effect of pre-roasting on leaching of laterite. Hydrometallurgy 99(1-2): 84-88.
19
[20] Tang, J., and Valix, M. (2006a). Leaching low-grade nickel ores by fungi metabolic acids, In Fell, C., Keller II, G.E. (Eds.), 2004 ECI Conference on Separations Technology VI: New Perspectives on Very Large Scale Operations. Berkeley Electronic Press. Paper 5, 16 pp.
20
[21] Önal, M.A.R., Topkaya, Y.A. (2014). Pressure acid leaching of Çaldag lateritic nickel ore: an alternative to heap leaching, Hydrometallurgy 1(42): 98-107.
21
[22] Bosecker, K. (1988). Bioleaching of non-sulfide minerals with heterotrophic microorganisms, In: Durand, G., Bobichon, L., Florent, J. (Eds.), Proceedings of the 8th International Biotechnology Symposium. Société Française de Microbiologie, Paris, 1106–1118.
22
[23] Chang, Y., Zhao, K., and Pesic, B. (2016). Selective leaching of nickel from pre-reduced limonitic laterite under moderate HPAL conditions-Part I: Dissolution, J MIN METALL B. 52, 127-134.
23
[24] McDonald, R. G., and Whittington, B. I. (2008). Atmospheric acid leaching of nickel laterites review. Part II. Chloride and biotechnologies, HYDROMETALLURGY. 91, 56-69.
24
[25] Astuti, W. (2015). Atmospheric leaching of Nickel from lowgrade Indonesian saprolite ores by biogenic citric acid, As partial fulfillment of the requirements for the degree of doctor of engineering, Kyushu University Fukuoka, Japan.
25
[26] Lee, S.O. (2005). Dissolution of iron oxides by oxalic acid, University of New South Wales.
26
[27] Stumm, W. (1992). Chemistry of the Solid–Water Interface, John Wiley and Sons Inc., New York.
27
[28] Cornell, R., Posner, A., Quirk, J. (1976). Kinetics and mechanisms of the acid dissolution of goethite (α-FeOOH). Journal of Inorganic and Nuclear Chemistry 38(3), 563-567.
28
[29] MacCarthy, J., Nosrati, A., Skinner, W., and Addai-Mensah, J. (2016). Atmospheric acid leaching mechanisms and kinetics and rheological studies of a low-grade saprolitic nickel laterite ore. HYDROMETALLURGY. 160: 26-37.
29
[30] Levenspiel, O. (1972). Chemical engineering reaction. Wiley-Eastern Limited, New York.
30
[31] Habashi, F. (1999). Kinetics of metallurgical processes. Metallurgie Extractive Quebec.
31
[32] Uçar, G. (2009). Kinetics of sphalerite dissolution by sodium chlorate in hydrochloric acid. Hydrometallurgy 95(1): 39-43.
32
[33] Tang, A., Su, L., Li, C., and Wei, W. (2010). Effect of mechanical activation on acid-leaching of kaolin residue. Appl Clay Sci. 48(3): 296-299.
33