تعیین مکان های کارا برای تخلیه پساب صنعتی کارخانه تغلیظ معدن مس سونگون با رویکردی چندهدفه بر اساس روش تحلیل پوششی داده های بازه ای

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی معدن، دانشکده فنی، دانشگاه ارومیه

2 گروه مهندسی معدن، دانشگاه صنعتی ارومیه

3 گروه مهندسی صنایع، دانشگاه صنعتی ارومیه

چکیده

امروزه جهت پاکسازی نقاط آلوده به انواع آلاینده­ها، روش­هایی نظیر فرآیندهای گیاه­پالایی کاربرد فراوانی یافته­اند. در همین راستا در این پژوهش، انتخاب مناسب‌ترین مکان و مقاوم‌ترین گونه کشت برای ایجاد مزارع کشاورزی با آبیاری از طریق پساب کارخانه تغلیظ مجتمع مس سونگون، مورد بررسی قرار گرفته است. علت مکان‌یابی مزارع در پیرامون معدن مذکور، علاوه بر استفاده بهینه از منابع آبی و پساب خروجی کارخانه، کاهش عوارض مخرب زیست­محیطی پساب کارخانه است که به عنوان تهدیدی برای جنگل­های ارسباران مجاور معدن، محسوب می­شود. در این پژوهش، از رویکردی چندهدفه برای مد نظر قرار دادن اهداف مهم مدیریتی نظیر کمینه­سازی هزینه و آب مصرفی و بیشینه‌سازی درآمد و کارایی نقاط منتخب استفاده می‌شود؛به­گونه‌ای که برای ارزیابی کارایی مکان‌های کاندیدا در مدل چندهدفه، از روش تحلیل پوششی توسعه­یافته بر مبنای روش تجمیع همزمان تحلیل پوششی داده‌ها با ماهیت داده‌های بازه‌ای بهره گرفته شده است. حل مدل برنامه­ریزی چندهدفه ارائه­شده، با یکپارچه‌سازی اهداف چهارگانه با استفاده از روش معیار‌ جامع وزن­دار انجام شده است. در نهایت با بررسی مطالعه­ی موردی، نحوه استفاده و تجزیه و تحلیل مدل ارائه­شده تشریح و اعتبارسنجی مدل برای معدن مس سونگون مورد تحلیل قرار گرفته است.

تازه های تحقیق

  1. حل مسأله مکان­یابی با استفاده از برنامه­ریزی چندهدفه شامل اهداف حداقل‌سازی انواع هزینه، حداکثرسازی درآمد و کارایی.
  2. مکان­یابی مزارع برای تخلیه پساب صنعتی کارخانه تغلیظ معدن مس سونگون بر اساس کارایی.
  3. استفاده از مدل تحلیل پوششی داده‌ها برای تجمیع اهمیت شاخص‌های ارزیابی هر مکان کاندیدا.
  4. استفاده از رویکرد برنامه‌ریزی چندهدفه مبتنی بر تحلیل پوششی داده­های بازه‌ای برای در نظر گرفتن عدم قطعیت در میزان شاخص‌های ارزیابی.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Determination of the efficient locations to discharge industrial wastewater of the Sungun Copper Mine concentration plant using multi-objective approach based on Interval Data Envelopment Analysis

نویسندگان [English]

  • Jafar Abdollahi Sharif 1
  • Amir Jafarpour 2
  • Samuel Yousefi 3
1 Dept. of Mining, Urmia University
2 Dept. of Mining, Urmia University of Technology
3 Dept. of Industrial, Urmia University of Technology
چکیده [English]

Summary
Currently, the environmental protection has found an important role in most countries. The effluents of the Sungun Copper Mine Concentration Plant (SCMCP) have the destructive effects. The location of farms, especially around the mine, should be performed based on technical and economical topics. In the present study, in order to the consideration of the main aims of the Mine management, a multi-objective approach is used. The effluents of SCMCP have some destructive effects on the Arasbaran forests. Thus, determination of the proper locations to discharge the wastewaters of the SCMCP is one of the important issues which should be considered. The Data Envelopment Analysis (DEA) method is an appropriate method for measuring the efficiency. The development of uncertainty in the world, indicates the importance of DEA method and its applications.
 
Introduction
Recently, various methods have been used to purify industrial and urban effluents also acidic mine drainages considering the importance of the environmental issues. In general, plants use several basic processes to do purification in the nature. In order to choose the best location of farm plants that are irrigated with wastewaters of the SCMCP, the management main goals should be considered.
 
Methodology and Approaches
In the present study, the efficient locations have been determined in order to discharge the industrial wastewaters of the SCMCP using multi-objective approach based on developed DEA method that is the Simultaneous Data Envelopment Analysis (SDEA) which uses the interval data. The integration of quartet goals with the weighted global criterion method is used to solve the proposed multi-objective model.
 
Results and Conclusions
How to use the model, the analysis process of the results, the description and the validation of the model for the Sungun Copper Mine have been studied as the case study. Due to the obtained results from solving the model and considering the different weight combinations, it can be said that the proposed model tries to provide a balance among different functions.

کلیدواژه‌ها [English]

  • Multi-objective programming
  • data envelopment analysis
  • Interval data
  • location farms
  • Sungun Copper Mine

مراجع

[1] Klimberg, R. K., & Ratick, S. J. (2008). Modeling data envelopment analysis (DEA) efficient location/allocation decisions. Computers & Operations Research, 35(2), 457-474.
[2] Azadeh, A., Ghaderi, S. F., & Maghsoudi, A. (2008). Location optimization of solar plants by an integrated hierarchical DEA PCA approach. Energy Policy, 36(10), 3993-4004.
[3] Afshari, H., Amin-Nayeri, M., & Jaafari, A. A. (2010). A multi-objective approach for multi-commodity location within distribution network design problem. In Proceedings of the International Multi Conference of Engineers and Computer Scientists. Hong Kong.
[4] Azadeh, A., Ghaderi, S. F., & Nasrollahi, M. R. (2011). Location optimization of wind plants in Iran by an integrated hierarchical Data Envelopment Analysis. Renewable Energy, 36(5), 1621-1631.
[5] Khadivi, M. R., & Ghomi, S. F. (2012). Solid waste facilities location using of analytical network process and data envelopment analysis approaches. Waste management, 32(6), 1258-1265.
[6] Mitropoulos, P., Mitropoulos, I., & Giannikos, I. (2013). Combining DEA with location analysis for the effective consolidation of services in the health sector.Computers & Operations Research, 40(9), 2241-2250.
[7] Karbasian, M., Dashti, M. & Asadollahi, A. (2012). The DEA combined model and location with respect to the distribution of sensitive facilities. Science and technology of the passive defense, 2(3), 161-167 (In Persian).
[8] Karbasian, M. & Dashti, M., (2012). Design of multi-objective location model, based on the multi-type distribution facilities and DEA with fuzzy goal programming solution. Production and operations management, 3(2), 98-110 (In Persian).
[9] Azadeh, A., Rahimi-Golkhandan, A., & Moghaddam, M. (2014). Location optimization of wind power generation–transmission systems under uncertainty using hierarchical fuzzy DEA: A case study. Renewable and Sustainable Energy Reviews, 30, 877-885.
[10] Babazadeh, R., Razmi, J., Pishvaee, M. S., & Rabbani, M. (2015). A non-radial DEA model for location optimization of Jatropha curcas L. cultivation. Industrial Crops and Products, 69, 197-203.
[11] Akbari, Sh. (2013). The importance of phytoremediation on sustainable development, healthy environment and analysis of the leading solutions. 2nd National Conference on Healthy Environment for Sustainable Agricultural Development. Hamedan. Iran. (In Persian).
[12] Masoumi, B. & Mohammadi, R. (2012). The principles of water treatment. First Edition. Tabriz: Forouzesh Publication. Iran (In Persian).
[13] Habashi, Fathi. (2006). Hydrometallurgy. Vol. 2. Translated by: Abdollahi, M & Ahafaee, S.Z., 2nd Edition. Shahroud: Shahroud University of Technology publication. Iran (In Persian).
[14] Vymazal, J., & Kröpfelová, L. (2008). Wastewater treatment in constructed wetlands with horizontal sub-surface flow (Vol. 14). Springer Science & Business Media.
[15] Tashayyoee, H.R., Mahdavi, M., Karkani, F., Ghelmani, S.V. & Ataeefar, H. (2011). Subsurface flow artificial wetland systems for wastewater treatment application with other countries and Iran. The health system. 7(6), 672-683 (In Persian).
[16] Moradi, H., Zarrabi, M.M & Hesari, P. (2013). The role of phytoremediation in reducing pollution of industrial sites with the approach of post-industrial landscape. 2nd National Conference on Environmental Protection Planning. Hamedan. Iran (In Persian).
[17] Heidari, F., Hosseini, S.M.  & Bahramifar, N. (2013). The introduction of phytoremediation technology. National Conference on Environmental Research. College of Shahid Mofatteh. Hahedan. Iran (In Persian).
[18] Färe, R., S. G., & Lovell, C. K. (1985). The Measurements of Efficiency of Production. Boston: Kluwer Nijhoff.
[19] Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 253-290.
[20] Lee, E. K., Ha, S., & Kim, S. K. (2001). Supplier selection and management system considering relationships in supply chain management. Engineering Management, IEEE Transactions on, 48(3), 307-318.
[21] Rezaee, M. J., Moini, A., & Asgari, F. H. A. (2012). Unified performance evaluation of health centers with integrated model of data envelopment analysis and bargaining game. Journal of medical systems, 36(6), 3805-3815.
[22] Hwang, S. N., Chen, C., Chen, Y., Lee, H. S., & Shen, P. D. (2013). Sustainable design performance evaluation with applications in the automobile industry: Focusing on inefficiency by undesirable factors. Omega, 41(3), 553-558.
[23] Shafiee, M., Lotfi, F. H., & Saleh, H. (2014). Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach. Applied Mathematical Modelling, 38(21), 5092-5112.
[24] Kim, K. T., Lee, D. J., Park, S. J., Zhang, Y., & Sultanov, A. (2015). Measuring the efficiency of the investment for renewable energy in Korea using data envelopment analysis. Renewable and Sustainable Energy Reviews, 47, 694-702.
[25] Rezaee, M. J., Izadbakhsh, H., & Yousefi, S. (2015). An improvement approach based on DEA-game theory for comparison of operational and spatial efficiencies in urban transportation systems. KSCE Journal of Civil Engineering, DOI: 10.1007/s12205-015-0345-9.
[26] Wanke, P., Barros, C. P., & Emrouznejad, A. (2016). Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks. European Journal of Operational Research, 249(1), 378-389.
[27] Zhu, J. (2003). Imprecise data envelopment analysis (IDEA): A review and improvement with an application. European Journal of Operational Research, 144(3), 513-529.
[28] Cooper, W. W., Park, K. S., & Yu, G. (1999). IDEA and AR-IDEA: Models for dealing with imprecise data in DEA. Management Science, 45(4), 597-607.
[29] Despotis, D. K., & Smirlis, Y. G. (2002). Data envelopment analysis with imprecise data. European Journal of Operational Research, 140(1), 24-36.
[30] Kao, C. (2006). Interval efficiency measures in data envelopment analysis with imprecise data. European Journal of Operational Research, 174(2), 1087-1099.
[31] Jahanshahloo, G. R., & Abbasian-Naghneh, S. (2011). Data envelopment analysis with imprecise data. Applied Mathematical Sciences, 5(61-64), 3089-3106.
[32] Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
[33] Bagherian, A. (2006). The concentration process of copper in the Sungun Copper Mine concentrator plant. Report of National Iranian Copper Industries (In Persian).
[34] Jafarpour, A., Abdollahi Sharif, J., Eivazi, A. & Aghajari, Gh. (2015). New biocompatible approaches to resolving the problem of effluent of Sungun Copper Mine. Third International Conference on Iran Open Pit Mines. Shahid Bahonar University. Kerman. Iran (In Persian).

فایل ها

سابقه مقاله

  • تاریخ دریافت: 12 آبان 1394
  • تاریخ بازنگری: 16 دی 1394
  • تاریخ پذیرش: 09 اسفند 1394

هم رسانی

ارجاع به این مقاله

آمار