A NOVEL HYBRID DECISION-MAKING MODEL: FUZZY AHP-TOPSIS APPROACH FOR PRIORITISING COPPER SMELTING PROCESSES
Keywords:
hybrid model, AHP, TOPSIS, fuzzy environment, copper smelting processes
Abstract
The construction of a copper smelting facility and its undisturbed and profitable business undoubtedly contribute to the development of each country’s economy. These facilities employ many workers and produce a large amount of copper, reducing imports and dependence on this important raw material, thereby improving the economic situation in a given country. More than a hundred copper smelters operate worldwide, many of which use different types of copper extraction processes. Strict legislation relating to ecology and environmental protection as well as stakeholder involvement in selecting and constructing copper smelting facilities limit the maximisation of short-term economic objectives. The prioritisation of technological processes for the extraction of copper must consider the impacts of often mutually opposing economic, technical and environmental objectives. No research from the available literature analyses the economic, technical and environmental parameters systematically. Studies have mainly dealt with exploring individual influences of factors through the use of one selection method. This paper presents the development of a novel hybrid AHP-TOPSIS model in fuzzy environments that will provide both informative decisions and optimum results of decision making.
References
1G. Artioli, I. Angelini, U. Tecchiati, A. Pedrotti, Eneolithic copper smelting slags in the Eastern Alps: Local patterns of metallurgical exploitation in the Copper Age, J. Archaeol. Sci., 63 (2015), 78-83, doi.org/10.1016/j.jas.2015.08.013
2H. Sievers, F. M. Meyer, Parameters influencing the efficiency of copper extraction, Erzmetall, 56 (2003) 8, 420–425.
3I. Nikolić, I. Milošević, N. Milijić, A. Jovanović, & I. Mihajlović, New Approach to Multi-Criteria Ranking of the Copper Concentrate Smelting Processes based on the PROMETHEE/GAIA Methodology, Acta. Polytech. Hung., 16 (2019) 1, 143-164, doi.org/10.12700/APH.16.1.2019.1.8
4J. Domagala-Dubiel, D. Janicki, G. Muzia, J. Lisicki, J. Ptaszny, J. Kulasa, Analysis of temperature distribution in laser alloying of pure copper, Mater. Tehnol., 56 (2022) 6, 629–635, doi:10.17222/mit.2022.551
5O. Herreros, R. Quiroz, E. Manzano, C. Bou, J. Vinals, Copper extraction from reverberatory and flash furnace slags by chlorine leaching. Hydrometallurgy, 49 (1998) 1-2, 87-101, doi.org/10.1016/S0304-386X(98)00010-3
6G. M. King, The evolution of technology for extractive metallurgy over the last 50 years – is the best yet to come? JOM, 59 (2007) 2, 21–27, doi.org/10.1007/s11837-007-0018-3
7M. Behun, B. Gavurova, A. Tkacova, A. Kotaskova, The impact of the manufacturing industry on the economic cycle of European Union countries, J. Compet., 10 (2018) 1, 23–39, doi.org/10.7441/joc.2018.01.02
8V. C. Jaunky, A cointegration and causality analysis of copper consumption and economic growth in rich countries, Resour. Policy., 38 (2013) 4, 628–639, doi.org/10.1016/j.resourpol.2013.10.001
9J. E. Tilton, The new view of minerals and economic growth, Econ. Rec., 65 (1989) 3, 265–278, doi.org/10.1111/j.1475-4932.1989.tb00935.x
10M. C. Roberts, Predicting metal consumption, Resour. Policy., 16 (1990) 1, 56–73, doi.org/10.1016/0301-4207(90)90018-7
11S. Ghosh, Steel consumption and economic growth: Evidence from India, Resour. Policy., 31 (2006) 1, 7–11, doi.org/10.1016/j.resourpol.2006.03.005
12K.-S. Huh, Steel consumption and economic growth in Korea: Long-term and short-term evidence, Resour. Policy., 36 (2011) 2, 107–113, doi.org/10.1016/j.resourpol.2011.01.005
13V. C. Jaunky, Aluminum consumption and economic growth: Evidence from high-income countries, Nat. Resour. Res., 21 (2012) 2, 265–278, doi.org/10.1007/s11053-012-9171-7
14A. Alonso-Ayuso, F. Carvallo, L. Escudero, M. Guignard, J. Pi, R. Puranmalka, A. Weintraub, Medium range optimization of copper extraction planning under uncertainty in future copper prices, Eur. J. Oper. Res., 233 (2014) 3, 711–726, doi.org/ 10.1016/j.ejor.2013.08.048
15B. Vučijak, S., Mirdžić-Kurtagić, I. Silajdžić. Multicriteria decision making in selecting best solid waste management scenario: A municipal case study from Bosnia and Herzegovina, J. Clean. Prod., 130 (2016), 166–174, doi.org/10.1016/j.jclepro.2015.11.030
16I. Belič, Neural network approximation strategies for wide range functions, (2015), Institute of Metals and Technology, doi.org/10.13140/RG.2.1.2932.4241
17R. Moskalyk, A. Alfantazi, Review of copper pyrometallurgical practice: Today and tomorrow, Miner. Eng., 16 (2003) 10, 893–919, doi.org/10.1016/j.mineng.2003.08.002
18J. Kapusta, JOM World Nonferrous Smelters Survey, Part I: Copper, JOM, 56 (2004) 7, 21–27, doi.org/10.1007/s11837-004-0086-6
19M. Schlesinger, M. King, K. Sole, W. Davenport, Extractive Metallurgy of Copper, 5th ed., Elsevier, Amsterdam, 2011
20I. Najdenov, Managing copper smelting and rafination processes for improving energy efficiency and economic feasibility, Doctoral dissertation, University of Belgrade, Faculty of Technology and Metallurgy, Belgrade, Serbia, 2013 (In Serbian).
21I. Nikolić, I. Milošević, N. Milijić, I. Mihajlović, Cleaner production and technical effectiveness: Multi-criteria Analysis of Copper Smelting Facilities. J. Clean. Prod., 215 (2019), 423-432, doi.org/10.1016/j.jclepro.2019.01.109
22A. Ishizaka, P. Nemery, Selecting the best statistical distribution with PROMETHEE and GAIA. Comput. Ind. Eng., 61 (2011) 4, 958–969, doi.org/10.1016/j.cie.2011.06.008
23N. Ruisheng, L. S. N. Mui, L. I. C. Hian, W. P. S. Chee, Bin Songa, Comparative eco-efficiency analyses of copper to copper bonding technologies, Procedia CIRP, 15 (2014), 96–104, doi.org/10.1016/j.procir.2014.06.027
24W. G. Davenport, M. King, M. Schlesinger, A. Biswas, Extractive Metallurgy of Copper, 4th ed., Elsevier, Amsterdam, 2002
25I. Najdenov, K. T. Rai, G. Kokeza, Aspects of energy reduction by autogenous copper production in the copper smelting plant Bor, Energy, 43 (2012) 1, 376–384, doi.org/10.1016/j.energy.2012.04.007
26Bridge, G. The social regulation of resource access and environmental impact: Production, nature and contradiction in the US copper industry, Geoforum, 31 (2000), 237– 256, doi.org/10.1016/S0016-7185(99)00046-9
27R. B. Gordon, Production residues in copper technological cycles, Resour. Conserv. Recycl., 36 (2002) 2, 87–106, doi.org/10.1016/S0921-3449(02)00019-8
28M., Rajčić-Vujasinović, V. Grekulović, Theory of Hydro and Electrometallurgical Processes, University of Belgrade, Technical Faculty in Bor, 2017 (In Serbian).
29W.G. Franzin, G. A. McFarlane, A. Lutz, Atmospheric fallout in the vicinity of a base metal smelter at Flin Flon, Manitoba, Environ. Sci. Technol., 13 (1979) 12, 1513–1522, doi.org/10.1021/es60160a014
30D. Filipou, P. St.German, & T. Grammatikopolus, Recovery of metal values from copper – arsenic minerals and other related resources, Min. Proc. Ext. Met. Rev., 28 (2007) 4, 247–298, doi.org/10.1080/08827500601013009
31J. C. Aznar, M. Richer-Lafleche, D. Cluis, Metal contamination in the lichen Alectoriasarmentosa near the copper smelter of Murdochville, Quebec. Environ. Pollut., 156 (2008) 1, 76–81, doi.org/10.1016/j.envpol.2007.12.037
32W. Kujawski, B. Pospiech, Processes and technologies for the recycling of spent fluorescent lamps, Pol. J. Chem. Techno.. 16 (2014) 3, 80–85, doi.org/10.2478/pjct-2014-0055
33R. Vračar, Theory and practice of non-ferrous metals. Belgrade, Serbia: Association of metallurgical engineers of Serbia, 2010 (In Serbian)
34I. Ilić, D. Bogdanović, D. Živković, N. Milošević, B. Todorović, Optimization of heavy metals total emission, case study: Bor (Serbia), Atmos. Res., 101 (2011) 1, 450–459, doi.org/10.1016/j.atmosres.2011.04.002
35V. Montenegro, H. Sano, T. Fujisawa, Recirculation of high arsenic content copper smelting dust to smelting and converting processes, Miner. Eng., 49 (2013), 184–189, doi.org/10.1016/j.mineng.2010.03.020
36D. R. Higgins, N. B. Gray, M. R. Davidson, Simulating particle agglomeration in the flash smelting reaction shaft, Miner. Eng., 22 (2009) 14, 1251–1265, doi.org/10.1016/j.mineng.2009.07.005
37J. Liu, W. Gui, Y. Xie, C. Yang, Dynamic modeling of copper flash smelting process at a smelter in China. Appl. Math. Model., 38 (2014) 7-8, 2206–2213, doi.org/10.1016/j.apm.2013.10.035
38Outokumpu. (2023). http://www.outokumpu.com/en/company/history/Pages/default.aspx, 12.06.2023
39Outotec. (2023). http://new.outotec.com, 15.03.2023
40USGS. (2023). US Geological Survey. https://mrdata.usgs.gov/copper/, 17.04.2023
41Isasmelt. (2017). https://www.glencoretechnology.com/en/technologies/isasmelt, 12.07.2023.
42P.E. Queneau, S. W. Marcuson, Oxygen pyrometallurgy at copper cliff––a half century of progress. JOM, 48 (1996) 1, 14–21. https://doi.org/10.1007/BF03221355
43Inco. (2023). http://www.inco.com.tr/about.php 2023
44T. Shibasaki, M. Hayashi, Y. Nishiyama, Recent operation at Naoshima with a larger Mitsubishi furnace line, in: C. Landolt (Ed.). Extractive Metallurgy of Copper, Nickel and Cobalt (the Paul E. Queneau International Symposium). Volume II: Copper and Nickel Smelter Operations, TMS, Warrendale, PA,1993, 1413–1428.
45O. Iida, M. Hayashi, M. Goto, Process designs on new smelter projects of the Mitsubishi continuous copper smelting and converting process. In: Proceedings of the Nickel–Cobalt 97 International Symposium, vol. 3, August 17–20, Sudbury, Canada, 1997, 499–511.
46Z. Asaki, T. Taniguchi, M. Hayashi, Kinetics of the reactions in the smelting furnace of the Mitsubishi process, JOM, 53 (2001) 5, 25–27, doi.org/10.1007/s11837-001-0203-8
47V. Fthenakis, W. Wang, H. C. Kim, Life cycle inventory analysis of the production of metals used in photovoltaics, Renew. Sust. Energ. Rev., 13 (2009) 3, 493–517, doi.org/10.1016/j.rser.2007.11.012
48J. L. Wang, Y. Z. Chen, W. H., Zhang, C. F. Zhang, Furnace structure analysis for copper flash continuous smelting based on numerical simulation. Trans. Nonferrous Met. Soc. China, 23 (2013) 12, 3799–3807, doi.org/10.1016/S1003-6326(13)62932-5
49H. Veldhuizen, B. Sippel, Mining discarded electronics, Ind. Environ, 17 (1994) 3, 7-11.
50J. Cui, L. Zhang, Metallurgical recovery of metals from electronic waste: A review, J. Hazard. Mater., 158 (2008), 228–256, doi.org/10.1016/j.jhazmat.2008.02.001
51L. G. Bergh, P. Chacana, C. Carrasco, Control strategy for a Teniente Converter, Miner. Eng., 18 (2005) 11, 1123–1126, doi.org/10.1016/j.mineng.2005.02.007
52A. Valencia, M. Rosales, R. Paredes, C. Leon, A. Moyano, Numerical and experimental investigation of the fluid dynamics in a Teniente type copper converter, Int. Commun. Heat. Mass., 33 (2006) 3, 302–310, doi.org/10.1016/j.icheatmasstransfer.2005.12.009
53M., Schaaf, Z. Gómez, A. Cipriano. Real-time hybrid predictive modeling of the Teniente Converter, J. Process. Control., 20 (2010) 3, 3–17, doi.org/10.1016/j.jprocont.2009.11.005
54C. Diaz, C. Landolt, A. Luraschi, C. J. Newman, Pyrometallurgy of Copper, Volume IV. Pergamon Press, New York, 1991
55F. Ullmann, Ullmann’sEncyclopaedia of Industrial Chemistry, 7th ed., Wiley-VCH,Weinheim, 471–524, 1995
56Ž. Stanković, Management of technological innovations in metallurgy of heavy non-ferrous metals, RTB Bor and Mining and Metallurgy Institute Bor, Bor, 2000
57A. Davidović, I. Najdenov, T. Husović Volkov, T. K. & Raić, Induction furnace without core: Design, operating parameters and applications. Livarstvo, 48 (2009) 2, 12–23, (In Serbian)
58M. Mohagheghi, M. Askari, Copper recovery from reverberatory furnace flue dust. Int. J. Miner. Process., 157 (2016), 205–209, doi.org/10.1016/j.minpro.2016.11.010
59T. L. Saaty, The Analytic Hierarchy Process, McGraw-Hill, New York, 1980
60J. M. Beynona, M. Mundaya, Considering the effects of imprecision and uncertainty in ecological footprint estimation: An approach in a fuzzy environment, Ecol. Econ., 67 (2008), 373 – 383, doi.org/10.1016/j.ecolecon.2008.07.005
61A. Singh, A. Prasher, Measuring healthcare service quality from patients’ perspective: using Fuzzy AHP application, Total Qual. Manag, 30 (2017) 3-4, 284-300, doi.org/10.1080/14783363.2017.1302794
62A. Emrouznejad, W. Ho, "Analytic Hierarchy Process and Fuzzy Set Theory” in Fuzzy Analytic Hierarchy Processed by Ali Emrouznejad, William Ho (eds.), 1-10, Taylor & Francis Inc., Portland, 2018
63N. Haber, M. Fargnoli, T. Sakao, Integrating QFD for product-service systems with the Kano model and fuzzy AHP, Total. Qual. Manag. Bus. Excell., 31 (2020) 9-10, 929-954, doi.org/10.1080/14783363.2018.1470897
64N. Nikolić, I. Jovanović, Đ. Nikolić, I. Mihajlović, P. Schulte, Investigation of the factors influencing SME failure as a function of its prevention and fast recovery after failure, Entrep. Res. J., 9 (2019) 3. 1-21, doi.org/10.1515/erj-2017-0030
65G.-N. Zhu, J. Hu, J. Qi, C.-C. Gu , Y.-H. Peng, An integrated AHP and VIKOR for design concept evaluation based on rough number, Adv. Eng. Inform., 29 (2015) 3, 408-418, doi.org/10.1016/j.aei.2015.01.010
66C. Kahraman, U. Cebeci, Z. Ulukan, Multi-criteria supplier selection using fuzzy AHP, Logist. Inf. Manag., 16 (2003) 6, 382–394, doi.org/10.1108/09576050310503367
67P. J. M. V. Laarhoven, W. Pedrycz, A fuzzy extension of Saaty’s priority theory, Fuzzy Sets. Syst., 11 (1983) 1-3, 229–241, doi.org/10.1016/S0165-0114(83)80082-7
68L. Anojkumar, M. Ilangkumaran, V. Sasirekha, Comparative analysis of MCDM methods for pipe material selection in sugar industry, Expert. Syst. Appl., 41 (2014), 2964–2980, doi.org/10.1016/j.eswa.2013.10.028
69D. Y. Chang, Applications of the extent analysis method on fuzzy AHP, Eur. J.Oper. Res., 95 (1996) 3, 649–655, doi.org/10.1016/0377-2217(95)00300-2
70T. C. Wang, Y. H. Chen, Applying consistent fuzzy preference relations to partnership selection. Omega, 35 (2007) 4, 384–388, doi.org/10.1016/j.omega.2005.07.007
71Y.-M. Wang, Y. Luo, Z. Hua, On the extent analysis method for fuzzy AHP and its applications, Eur. J.Oper. Res., 186 (2008) 2, 735–747, doi.org/10.1016/j.ejor.2007.01.050
72Y. L. Hsu, C.H. Lee, V. B. Kreng, The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection, Expert. Syst. Appl., 37 (2010) 1, 419–425, doi.org/10.1016/j.eswa.2009.05.068
73N.Y. Pehlivan, T. Paksoy, A. Çalik, “Comparison of Methods in FAHP with Application in Supplier Selection” in Fuzzy Analytic Hierarchy Processed by Ali Emrouznejad and William Ho, 45–75, 2018
74D. Kannan, R. Khodaverdi, L. Olfat, A. Jafarian, A. Diabat, Integrated fuzzy multi criteria decision making method and multiobjective programming approach for supplier selection and order allocation in a green supply chain, J. Clean. Prod., 47 (2013), 355–367, doi.org/10.1016/j.jclepro.2013.02.010
75G. Kabir, R. S. Sumi, Power substation location selection using fuzzy analytic hierarchy process and PROMETHEE: A case study from Bangladesh, Energy, 72 (2014), 717-730, doi.org/10.1016/j.energy.2014.05.098
76T. S. Liou, M. J. Wang, Ranking fuzzy numbers with integral value, Fuzzy Sets. Syst., 50 (1992) 3, 247–255, doi.org/10.1016/0165-0114(92)90223-Q
77C.G. Sen, G. Çinar, Evaluation and pre-allocation of operators with multiple skills: A combined fuzzy AHP and maxemin approach. Expert. Syst. Appl., 37 (2010) 3, 2043–2053, doi.org/10.1016/j.eswa.2009.06.075
78C. L. Hwang, K. Yoon, Multiple Attribute Decision Making: Methods and Applications, A State of the Art Survey, Springer-Verlag, New York, 1981
79J. M. Benitez, J. C. Martin, C. Roman, Using fuzzy number for measuring quality of service in the hotel industry, Tour. Manag., 28 (2007) 2, 544-555, doi.org/10.1016/j.tourman.2006.04.018
80A. C. Kutlu, M. Ekmekcioglu, Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP, Expert. Syst. Appl., 39 (2012) 1, 61–67, doi.org/10.1016/j.eswa.2011.06.044
81M. Behzadian, K. Otaghsara, M. Yazdani, J. Ignatius, A state-of-art survey of TOPSIS applications, Expert. Syst. Appl., 39 (2012), 13051–13069, doi.org/10.1016/j.eswa.2012.05.056
82F. T. S. Chan, N. Kumar, Global supplier development considering risk factors using fuzzy extended AHP-based approach, OMEGA, 35 (2007) 4, 417–431, doi.org/10.1016/j.omega.2005.08.004
83F. Torfi, R. Z. Farahani, S. Rezapour, Fuzzy AHP to determine the relative weights of evaluation criteria and Fuzzy TOPSIS to rank the alternatives, Appl. Soft. Comput., 10 (2010) 2, 520–528, doi.org/10.1016/j.asoc.2009.08.021
84P.Pivodová, E. Juřičková, R. Bobák, Design of Process and Organizational Innovation Application Methodology. In: Proceedings of the 9th European Conference on Innovation and Entrepreneurship, Belfast, United Kingdom, 547–555, 2014
2H. Sievers, F. M. Meyer, Parameters influencing the efficiency of copper extraction, Erzmetall, 56 (2003) 8, 420–425.
3I. Nikolić, I. Milošević, N. Milijić, A. Jovanović, & I. Mihajlović, New Approach to Multi-Criteria Ranking of the Copper Concentrate Smelting Processes based on the PROMETHEE/GAIA Methodology, Acta. Polytech. Hung., 16 (2019) 1, 143-164, doi.org/10.12700/APH.16.1.2019.1.8
4J. Domagala-Dubiel, D. Janicki, G. Muzia, J. Lisicki, J. Ptaszny, J. Kulasa, Analysis of temperature distribution in laser alloying of pure copper, Mater. Tehnol., 56 (2022) 6, 629–635, doi:10.17222/mit.2022.551
5O. Herreros, R. Quiroz, E. Manzano, C. Bou, J. Vinals, Copper extraction from reverberatory and flash furnace slags by chlorine leaching. Hydrometallurgy, 49 (1998) 1-2, 87-101, doi.org/10.1016/S0304-386X(98)00010-3
6G. M. King, The evolution of technology for extractive metallurgy over the last 50 years – is the best yet to come? JOM, 59 (2007) 2, 21–27, doi.org/10.1007/s11837-007-0018-3
7M. Behun, B. Gavurova, A. Tkacova, A. Kotaskova, The impact of the manufacturing industry on the economic cycle of European Union countries, J. Compet., 10 (2018) 1, 23–39, doi.org/10.7441/joc.2018.01.02
8V. C. Jaunky, A cointegration and causality analysis of copper consumption and economic growth in rich countries, Resour. Policy., 38 (2013) 4, 628–639, doi.org/10.1016/j.resourpol.2013.10.001
9J. E. Tilton, The new view of minerals and economic growth, Econ. Rec., 65 (1989) 3, 265–278, doi.org/10.1111/j.1475-4932.1989.tb00935.x
10M. C. Roberts, Predicting metal consumption, Resour. Policy., 16 (1990) 1, 56–73, doi.org/10.1016/0301-4207(90)90018-7
11S. Ghosh, Steel consumption and economic growth: Evidence from India, Resour. Policy., 31 (2006) 1, 7–11, doi.org/10.1016/j.resourpol.2006.03.005
12K.-S. Huh, Steel consumption and economic growth in Korea: Long-term and short-term evidence, Resour. Policy., 36 (2011) 2, 107–113, doi.org/10.1016/j.resourpol.2011.01.005
13V. C. Jaunky, Aluminum consumption and economic growth: Evidence from high-income countries, Nat. Resour. Res., 21 (2012) 2, 265–278, doi.org/10.1007/s11053-012-9171-7
14A. Alonso-Ayuso, F. Carvallo, L. Escudero, M. Guignard, J. Pi, R. Puranmalka, A. Weintraub, Medium range optimization of copper extraction planning under uncertainty in future copper prices, Eur. J. Oper. Res., 233 (2014) 3, 711–726, doi.org/ 10.1016/j.ejor.2013.08.048
15B. Vučijak, S., Mirdžić-Kurtagić, I. Silajdžić. Multicriteria decision making in selecting best solid waste management scenario: A municipal case study from Bosnia and Herzegovina, J. Clean. Prod., 130 (2016), 166–174, doi.org/10.1016/j.jclepro.2015.11.030
16I. Belič, Neural network approximation strategies for wide range functions, (2015), Institute of Metals and Technology, doi.org/10.13140/RG.2.1.2932.4241
17R. Moskalyk, A. Alfantazi, Review of copper pyrometallurgical practice: Today and tomorrow, Miner. Eng., 16 (2003) 10, 893–919, doi.org/10.1016/j.mineng.2003.08.002
18J. Kapusta, JOM World Nonferrous Smelters Survey, Part I: Copper, JOM, 56 (2004) 7, 21–27, doi.org/10.1007/s11837-004-0086-6
19M. Schlesinger, M. King, K. Sole, W. Davenport, Extractive Metallurgy of Copper, 5th ed., Elsevier, Amsterdam, 2011
20I. Najdenov, Managing copper smelting and rafination processes for improving energy efficiency and economic feasibility, Doctoral dissertation, University of Belgrade, Faculty of Technology and Metallurgy, Belgrade, Serbia, 2013 (In Serbian).
21I. Nikolić, I. Milošević, N. Milijić, I. Mihajlović, Cleaner production and technical effectiveness: Multi-criteria Analysis of Copper Smelting Facilities. J. Clean. Prod., 215 (2019), 423-432, doi.org/10.1016/j.jclepro.2019.01.109
22A. Ishizaka, P. Nemery, Selecting the best statistical distribution with PROMETHEE and GAIA. Comput. Ind. Eng., 61 (2011) 4, 958–969, doi.org/10.1016/j.cie.2011.06.008
23N. Ruisheng, L. S. N. Mui, L. I. C. Hian, W. P. S. Chee, Bin Songa, Comparative eco-efficiency analyses of copper to copper bonding technologies, Procedia CIRP, 15 (2014), 96–104, doi.org/10.1016/j.procir.2014.06.027
24W. G. Davenport, M. King, M. Schlesinger, A. Biswas, Extractive Metallurgy of Copper, 4th ed., Elsevier, Amsterdam, 2002
25I. Najdenov, K. T. Rai, G. Kokeza, Aspects of energy reduction by autogenous copper production in the copper smelting plant Bor, Energy, 43 (2012) 1, 376–384, doi.org/10.1016/j.energy.2012.04.007
26Bridge, G. The social regulation of resource access and environmental impact: Production, nature and contradiction in the US copper industry, Geoforum, 31 (2000), 237– 256, doi.org/10.1016/S0016-7185(99)00046-9
27R. B. Gordon, Production residues in copper technological cycles, Resour. Conserv. Recycl., 36 (2002) 2, 87–106, doi.org/10.1016/S0921-3449(02)00019-8
28M., Rajčić-Vujasinović, V. Grekulović, Theory of Hydro and Electrometallurgical Processes, University of Belgrade, Technical Faculty in Bor, 2017 (In Serbian).
29W.G. Franzin, G. A. McFarlane, A. Lutz, Atmospheric fallout in the vicinity of a base metal smelter at Flin Flon, Manitoba, Environ. Sci. Technol., 13 (1979) 12, 1513–1522, doi.org/10.1021/es60160a014
30D. Filipou, P. St.German, & T. Grammatikopolus, Recovery of metal values from copper – arsenic minerals and other related resources, Min. Proc. Ext. Met. Rev., 28 (2007) 4, 247–298, doi.org/10.1080/08827500601013009
31J. C. Aznar, M. Richer-Lafleche, D. Cluis, Metal contamination in the lichen Alectoriasarmentosa near the copper smelter of Murdochville, Quebec. Environ. Pollut., 156 (2008) 1, 76–81, doi.org/10.1016/j.envpol.2007.12.037
32W. Kujawski, B. Pospiech, Processes and technologies for the recycling of spent fluorescent lamps, Pol. J. Chem. Techno.. 16 (2014) 3, 80–85, doi.org/10.2478/pjct-2014-0055
33R. Vračar, Theory and practice of non-ferrous metals. Belgrade, Serbia: Association of metallurgical engineers of Serbia, 2010 (In Serbian)
34I. Ilić, D. Bogdanović, D. Živković, N. Milošević, B. Todorović, Optimization of heavy metals total emission, case study: Bor (Serbia), Atmos. Res., 101 (2011) 1, 450–459, doi.org/10.1016/j.atmosres.2011.04.002
35V. Montenegro, H. Sano, T. Fujisawa, Recirculation of high arsenic content copper smelting dust to smelting and converting processes, Miner. Eng., 49 (2013), 184–189, doi.org/10.1016/j.mineng.2010.03.020
36D. R. Higgins, N. B. Gray, M. R. Davidson, Simulating particle agglomeration in the flash smelting reaction shaft, Miner. Eng., 22 (2009) 14, 1251–1265, doi.org/10.1016/j.mineng.2009.07.005
37J. Liu, W. Gui, Y. Xie, C. Yang, Dynamic modeling of copper flash smelting process at a smelter in China. Appl. Math. Model., 38 (2014) 7-8, 2206–2213, doi.org/10.1016/j.apm.2013.10.035
38Outokumpu. (2023). http://www.outokumpu.com/en/company/history/Pages/default.aspx, 12.06.2023
39Outotec. (2023). http://new.outotec.com, 15.03.2023
40USGS. (2023). US Geological Survey. https://mrdata.usgs.gov/copper/, 17.04.2023
41Isasmelt. (2017). https://www.glencoretechnology.com/en/technologies/isasmelt, 12.07.2023.
42P.E. Queneau, S. W. Marcuson, Oxygen pyrometallurgy at copper cliff––a half century of progress. JOM, 48 (1996) 1, 14–21. https://doi.org/10.1007/BF03221355
43Inco. (2023). http://www.inco.com.tr/about.php 2023
44T. Shibasaki, M. Hayashi, Y. Nishiyama, Recent operation at Naoshima with a larger Mitsubishi furnace line, in: C. Landolt (Ed.). Extractive Metallurgy of Copper, Nickel and Cobalt (the Paul E. Queneau International Symposium). Volume II: Copper and Nickel Smelter Operations, TMS, Warrendale, PA,1993, 1413–1428.
45O. Iida, M. Hayashi, M. Goto, Process designs on new smelter projects of the Mitsubishi continuous copper smelting and converting process. In: Proceedings of the Nickel–Cobalt 97 International Symposium, vol. 3, August 17–20, Sudbury, Canada, 1997, 499–511.
46Z. Asaki, T. Taniguchi, M. Hayashi, Kinetics of the reactions in the smelting furnace of the Mitsubishi process, JOM, 53 (2001) 5, 25–27, doi.org/10.1007/s11837-001-0203-8
47V. Fthenakis, W. Wang, H. C. Kim, Life cycle inventory analysis of the production of metals used in photovoltaics, Renew. Sust. Energ. Rev., 13 (2009) 3, 493–517, doi.org/10.1016/j.rser.2007.11.012
48J. L. Wang, Y. Z. Chen, W. H., Zhang, C. F. Zhang, Furnace structure analysis for copper flash continuous smelting based on numerical simulation. Trans. Nonferrous Met. Soc. China, 23 (2013) 12, 3799–3807, doi.org/10.1016/S1003-6326(13)62932-5
49H. Veldhuizen, B. Sippel, Mining discarded electronics, Ind. Environ, 17 (1994) 3, 7-11.
50J. Cui, L. Zhang, Metallurgical recovery of metals from electronic waste: A review, J. Hazard. Mater., 158 (2008), 228–256, doi.org/10.1016/j.jhazmat.2008.02.001
51L. G. Bergh, P. Chacana, C. Carrasco, Control strategy for a Teniente Converter, Miner. Eng., 18 (2005) 11, 1123–1126, doi.org/10.1016/j.mineng.2005.02.007
52A. Valencia, M. Rosales, R. Paredes, C. Leon, A. Moyano, Numerical and experimental investigation of the fluid dynamics in a Teniente type copper converter, Int. Commun. Heat. Mass., 33 (2006) 3, 302–310, doi.org/10.1016/j.icheatmasstransfer.2005.12.009
53M., Schaaf, Z. Gómez, A. Cipriano. Real-time hybrid predictive modeling of the Teniente Converter, J. Process. Control., 20 (2010) 3, 3–17, doi.org/10.1016/j.jprocont.2009.11.005
54C. Diaz, C. Landolt, A. Luraschi, C. J. Newman, Pyrometallurgy of Copper, Volume IV. Pergamon Press, New York, 1991
55F. Ullmann, Ullmann’sEncyclopaedia of Industrial Chemistry, 7th ed., Wiley-VCH,Weinheim, 471–524, 1995
56Ž. Stanković, Management of technological innovations in metallurgy of heavy non-ferrous metals, RTB Bor and Mining and Metallurgy Institute Bor, Bor, 2000
57A. Davidović, I. Najdenov, T. Husović Volkov, T. K. & Raić, Induction furnace without core: Design, operating parameters and applications. Livarstvo, 48 (2009) 2, 12–23, (In Serbian)
58M. Mohagheghi, M. Askari, Copper recovery from reverberatory furnace flue dust. Int. J. Miner. Process., 157 (2016), 205–209, doi.org/10.1016/j.minpro.2016.11.010
59T. L. Saaty, The Analytic Hierarchy Process, McGraw-Hill, New York, 1980
60J. M. Beynona, M. Mundaya, Considering the effects of imprecision and uncertainty in ecological footprint estimation: An approach in a fuzzy environment, Ecol. Econ., 67 (2008), 373 – 383, doi.org/10.1016/j.ecolecon.2008.07.005
61A. Singh, A. Prasher, Measuring healthcare service quality from patients’ perspective: using Fuzzy AHP application, Total Qual. Manag, 30 (2017) 3-4, 284-300, doi.org/10.1080/14783363.2017.1302794
62A. Emrouznejad, W. Ho, "Analytic Hierarchy Process and Fuzzy Set Theory” in Fuzzy Analytic Hierarchy Processed by Ali Emrouznejad, William Ho (eds.), 1-10, Taylor & Francis Inc., Portland, 2018
63N. Haber, M. Fargnoli, T. Sakao, Integrating QFD for product-service systems with the Kano model and fuzzy AHP, Total. Qual. Manag. Bus. Excell., 31 (2020) 9-10, 929-954, doi.org/10.1080/14783363.2018.1470897
64N. Nikolić, I. Jovanović, Đ. Nikolić, I. Mihajlović, P. Schulte, Investigation of the factors influencing SME failure as a function of its prevention and fast recovery after failure, Entrep. Res. J., 9 (2019) 3. 1-21, doi.org/10.1515/erj-2017-0030
65G.-N. Zhu, J. Hu, J. Qi, C.-C. Gu , Y.-H. Peng, An integrated AHP and VIKOR for design concept evaluation based on rough number, Adv. Eng. Inform., 29 (2015) 3, 408-418, doi.org/10.1016/j.aei.2015.01.010
66C. Kahraman, U. Cebeci, Z. Ulukan, Multi-criteria supplier selection using fuzzy AHP, Logist. Inf. Manag., 16 (2003) 6, 382–394, doi.org/10.1108/09576050310503367
67P. J. M. V. Laarhoven, W. Pedrycz, A fuzzy extension of Saaty’s priority theory, Fuzzy Sets. Syst., 11 (1983) 1-3, 229–241, doi.org/10.1016/S0165-0114(83)80082-7
68L. Anojkumar, M. Ilangkumaran, V. Sasirekha, Comparative analysis of MCDM methods for pipe material selection in sugar industry, Expert. Syst. Appl., 41 (2014), 2964–2980, doi.org/10.1016/j.eswa.2013.10.028
69D. Y. Chang, Applications of the extent analysis method on fuzzy AHP, Eur. J.Oper. Res., 95 (1996) 3, 649–655, doi.org/10.1016/0377-2217(95)00300-2
70T. C. Wang, Y. H. Chen, Applying consistent fuzzy preference relations to partnership selection. Omega, 35 (2007) 4, 384–388, doi.org/10.1016/j.omega.2005.07.007
71Y.-M. Wang, Y. Luo, Z. Hua, On the extent analysis method for fuzzy AHP and its applications, Eur. J.Oper. Res., 186 (2008) 2, 735–747, doi.org/10.1016/j.ejor.2007.01.050
72Y. L. Hsu, C.H. Lee, V. B. Kreng, The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection, Expert. Syst. Appl., 37 (2010) 1, 419–425, doi.org/10.1016/j.eswa.2009.05.068
73N.Y. Pehlivan, T. Paksoy, A. Çalik, “Comparison of Methods in FAHP with Application in Supplier Selection” in Fuzzy Analytic Hierarchy Processed by Ali Emrouznejad and William Ho, 45–75, 2018
74D. Kannan, R. Khodaverdi, L. Olfat, A. Jafarian, A. Diabat, Integrated fuzzy multi criteria decision making method and multiobjective programming approach for supplier selection and order allocation in a green supply chain, J. Clean. Prod., 47 (2013), 355–367, doi.org/10.1016/j.jclepro.2013.02.010
75G. Kabir, R. S. Sumi, Power substation location selection using fuzzy analytic hierarchy process and PROMETHEE: A case study from Bangladesh, Energy, 72 (2014), 717-730, doi.org/10.1016/j.energy.2014.05.098
76T. S. Liou, M. J. Wang, Ranking fuzzy numbers with integral value, Fuzzy Sets. Syst., 50 (1992) 3, 247–255, doi.org/10.1016/0165-0114(92)90223-Q
77C.G. Sen, G. Çinar, Evaluation and pre-allocation of operators with multiple skills: A combined fuzzy AHP and maxemin approach. Expert. Syst. Appl., 37 (2010) 3, 2043–2053, doi.org/10.1016/j.eswa.2009.06.075
78C. L. Hwang, K. Yoon, Multiple Attribute Decision Making: Methods and Applications, A State of the Art Survey, Springer-Verlag, New York, 1981
79J. M. Benitez, J. C. Martin, C. Roman, Using fuzzy number for measuring quality of service in the hotel industry, Tour. Manag., 28 (2007) 2, 544-555, doi.org/10.1016/j.tourman.2006.04.018
80A. C. Kutlu, M. Ekmekcioglu, Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP, Expert. Syst. Appl., 39 (2012) 1, 61–67, doi.org/10.1016/j.eswa.2011.06.044
81M. Behzadian, K. Otaghsara, M. Yazdani, J. Ignatius, A state-of-art survey of TOPSIS applications, Expert. Syst. Appl., 39 (2012), 13051–13069, doi.org/10.1016/j.eswa.2012.05.056
82F. T. S. Chan, N. Kumar, Global supplier development considering risk factors using fuzzy extended AHP-based approach, OMEGA, 35 (2007) 4, 417–431, doi.org/10.1016/j.omega.2005.08.004
83F. Torfi, R. Z. Farahani, S. Rezapour, Fuzzy AHP to determine the relative weights of evaluation criteria and Fuzzy TOPSIS to rank the alternatives, Appl. Soft. Comput., 10 (2010) 2, 520–528, doi.org/10.1016/j.asoc.2009.08.021
84P.Pivodová, E. Juřičková, R. Bobák, Design of Process and Organizational Innovation Application Methodology. In: Proceedings of the 9th European Conference on Innovation and Entrepreneurship, Belfast, United Kingdom, 547–555, 2014
Published
2024-04-02
How to Cite
1.
Nikolić I, Stojanović A, Mitrović M. A NOVEL HYBRID DECISION-MAKING MODEL: FUZZY AHP-TOPSIS APPROACH FOR PRIORITISING COPPER SMELTING PROCESSES. MatTech [Internet]. 2024Apr.2 [cited 2024Dec.10];58(2):147–157. Available from: https://mater-tehnol.si/index.php/MatTech/article/view/1037
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