بررسی تأثیر معیارهای محیطی در کارایی فرآیند گیاه‌پالایی پساب‌های معدنی با استفاده ازروش نقشه شناختی(مطالعه موردی: معدن مس سونگون)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه صنعتی ارومیه

2 دانشگاه ارومیه

10.29252/anm.8.15.11

چکیده

روند رو به رشد فعالیت­های معدنی و عدم رعایت الزامات زیست­محیطی، باعث شده تا مقادیر هنگفتی از آلاینده­های صنایع معدنی به واسطه دفع غیراصولی ضایعات و عدم استفاده از فناوری­های نوین، از طریق آب وارد طبیعت شوند. در این میان، پساب کارخانه­های تغلیظ معادن، در کنار تخریب منابع طبیعی، فشاری مضاعف بر اکوسیستم تحمیل می­کند. در سال­های اخیر، به کارگیری فرآیندهای زیست‌سازگار گیاه‌پالایی در تصفیه پساب کارخانه­های تغلیظ معادن، نتایج قابل توجهی را در پی داشته است. بررسی کارایی فرآیند گیاه­پالایی و شناسایی معیارهای مختلف محیطی مؤثر بر آن، نه تنها کاربردهای این فناوری نوین را آشکار می­سازد، بلکه طراحان این سیستم­های طبیعی را در خصوص اجرایی کردن آن یاری می‌کند. در این مطالعه، با توجه به اینکه در دنیای واقعی، معیارهای محیطی بر روی همدیگر نیز اثر گذراند، باید روابط متقابل بین این معیارها برای دستیابی به نتایج واقعی در نظر گرفته شود. از این رو،در این پژوهش، از روش نقشه شناختی برای بررسی اثرگذاری معیارهای محیطی بر روی کارایی گیاه­پالایی با مد نظر قرار دادن روابط میان آنها استفاده شده است. در قالب مطالعه موردی نیز با توجه به اهمیت منطقه حفاظت­شده ارسباران و تأثیر عوارض مخرب سد باطله معدن مس سونگون بر آن، گیاه­پالایی پساب کارخانه تغلیظ این معدن مورد بررسی قرار گرفته است. نتایج نشان می­دهد که «میزان فلزات سنگین موجود در سد باطله» با وزن نسبی 37/0، «میزان فلز محتوی کانسنگ» با وزن نسبی 34/0 و «میزان فلزات سنگین محلول در پساب» با وزن نسبی 33/0 مهم­ترین معیارهای محیطی اثرگذار درکارایی فرآیند گیاه­پالایی هستند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Study of the effects of the environmental factors on mining effluents phytoremediation process efficiency using cognitive map method (Case study: Sungun copper mine)

نویسندگان [English]

  • amir Jafarpour 1
  • Jafar Abdollahei sharif 2
  • Samuel Yousefi 1
  • Mustafa Jahangoshai Rezaee 1
1 urmia university of Technology
2 urmia university
چکیده [English]

Summary
Over the years, many expensive chemical and physical methods are used to purge ecosystems from the environmental pollutions caused by various contaminants through the mining activities. Nowadays the biological resources are used to clean up the contaminated areas with different pollutants. One of the most inexpensive methods is the phytoremediation process to remove the contaminants from wastewater and soil.This study is considering the real conditions in the real world, therefore, since the environmental criteria have an effect on each other, we should consider the interrelationship between these criteria in order to achieve the real results.
 
Introduction
The growing trend of mining activities and non-compliance with environmental requirements has caused large amounts of contaminants from the mining industry to be inserted into the water via unprocessed waste disposal and the lack of applying the new technologies. In the recent years, the application of plant bioprocessing processes in wastewater treatment of concenteration plants has resulted in some significant results.Investigating the efficacy of the phytoremediation process and identifying the different effective environmental criteria, not only reveals the uses of this new technology, but also helps designers of these natural systems to implement it.
 
Methodology and Approaches
In this study, the cognitive map is used to identify the relationships between the environmental factors affecting the efficiency of phytoremediation mining wastewaters; also this investigation tries to clarify how each factor affects the efficiency of phytoremediation. Therefore, in this study, the cognitive map method and hybrid Nonlinear Hebbian-differential evolution (NHL-DE) algorithm have been used to examine the effectiveness of environmental criteria on phytoremediation efficiency considering the relations between them.
 
Results and Conclusions
In the case of Sungun copper mine tailing dam (in Arasbaran protected area) the phytoremediation of the mentioned concentration plant is analyzed based on the destructive impacts. The results showed that the amount of heavy metals in tailing dam, the amount of metal containing ore and the amount of heavy metals dissolved in the wastewater with the relative weights equal to 0.37, 0.34 and 0.33 (respectively) are the most important environmental factors affecting the operation of thephytoremediation processes.

کلیدواژه‌ها [English]

  • Phytoremediation
  • Environmental Factors
  • Cognitive Map
  • Sungun Copper Mine
[1]           Dhir, B. (2013). Phytoremediation: Role of Aquatic Plants in Environmental Clean-up. Springer.
[2] Lasat, M. M. (2002). Phytoextraction of toxic metals. Journal of environmental quality, 31(1), 109-120.
[3]           Harvey, P. J., Campanella, B. F., Castro, P. M., Harms, H., Lichtfouse, E., Schäffner, A. R., & Werck-Reichhart, D. (2002). Phytoremediation of polyaromatic hydrocarbons, anilines and phenols. Environmental Science and Pollution Research, 9(1), 29-47.
[4]           Crites, R. W., Middlebrooks, E. J., & Reed, S. C. (2006). Natural Wastewater Systems; New York: CRC / Taylor & Francis.
[5]           Vymazal, J., & Kröpfelová, L. (2008). Wastewater treatment in constructed wetlands with horizontal sub-surface flow (Vol. 14). Springer Science & Business Media.
[6]           Jafarpour, A., Sharif, J. A., & Eivazi, A. (2017). Reducing Destructive Environmental Impacts of Sungun Copper Mine Effluents with using of Phytoremediation Processes. International Journal of Pure & Applied Bioscience, 5(2), 43-55.
[7]           Zhang, H., Song, J., Su, C., & He, M. (2013). Human attitudes in environmental management: Fuzzy Cognitive Maps and policy option simulations analysis for a coal-mine ecosystem in China. Journal of environmental management, 115, 227-234.
[8]           Tikkanen, J., Isokääntä, T., Pykäläinen, J., & Leskinen, P. (2006). Applying cognitive mapping approach to explore the objective-structure of forest owners in a Northern Finnish case area. Forest Policy and Economics, 9(2), 139-152.
[9]           Mourhir, A., Rachidi, T., Papageorgiou, E. I., Karim, M., & Alaoui, F. S. (2016). A cognitive map framework to support integrated environmental assessment. Environmental Modelling & Software, 77, 81-94.
[10]         Lee, K. C., Lee, H., Lee, N., & Lim, J. (2013). An agent-based fuzzy cognitive map approach to the strategic marketing planning for industrial firms. Industrial Marketing Management, 42(4), 552-563.
[11]         Büyüközkan, G., & Vardaloğlu, Z. (2012). Analyzing of CPFR success factors using fuzzy cognitive maps in retail industry. Expert Systems with Applications, 39(12), 10438-10455.
[12]         Rezaee, M.J., Yousefi, S., Baghery, M., & Kakaei, S. (2017). A Decision Making Framework for Evaluating Suppliers of Automotive Parts Industry Based on Cognitive Map. Journal of Industrial Engineering, 51 (1), 59-75. (In Persian with English abstract)
[13]         Kyriakarakos, G., Patlitzianas, K., Damasiotis, M., & Papastefanakis, D. (2014). A fuzzy cognitive maps decision support system for renewables local planning. Renewable and Sustainable Energy Reviews, 39, 209-222.
[14]         Kyriakarakos, G., Dounis, A. I., Arvanitis, K. G., & Papadakis, G. (2017). Design of a Fuzzy Cognitive Maps variable-load energy management system for autonomous PV-reverse osmosis desalination systems: A simulation survey. Applied Energy, 187, 575-584.
[15]         Upham, P., & Pérez, J. G. (2015). A cognitive mapping approach to understanding public objection to energy infrastructure: The case of wind power in Galicia, Spain. Renewable Energy, 83, 587-596.
[16]         Azadeh, A., Ziaei, B., & Moghaddam, M. (2012). A hybrid fuzzy regression-fuzzy cognitive map algorithm for forecasting and optimization of housing market fluctuations. Expert Systems with Applications, 39(1), 298-315.
[17]         Olazabal, M., & Pascual, U. (2016). Use of fuzzy cognitive maps to study urban resilience and transformation. Environmental Innovation and Societal Transitions, 18, 18-40.
[18]         Yousefi, S., Kakaei, S., & Rezaee, M.J. (2017).A hybrid method using fuzzy cognitive map- DEA to study the delays in construction projects. Journal Industrial Management Studies, 15(45), 177-207. (In Persian with English abstract)
[19]         Zhang, L., Chettupuzha, A. A., Chen, H., Wu, X., & AbouRizk, S. M. (2017). Fuzzy cognitive maps enabled root cause analysis in complex projects. Applied Soft Computing, 57, 235-249.
[20]         Papageorgiou, E. I. (2011). A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques. Applied Soft Computing, 11(1), 500-513.
[21]         Rezaee, M. J., Yousefi, S., & Hayati, J. (2016). A decision system using fuzzy cognitive map and multi-group data envelopment analysis to estimate hospitals’ outputs level. Neural Computing and Applications, doi:10.1007/s00521-016-2478-2.
[22]         Salmeron, J. L., Rahimi, S. A., Navali, A. M., & Sadeghpour, A. (2017). Medical diagnosis of Rheumatoid Arthritis using data driven PSO–FCM with scarce datasets. Neurocomputing, 232, 104-112.
[23]         Rezaee, M. J. and Yousefi, S. (2017). An intelligent decision making approach for identifying and analyzing airport risks. Journal of Air Transport Management, doi: 10.1016/j.jairtraman.2017.06.013.
[24]         Papageorgiou, E. I., Stylios, C., & Groumpos, P. P. (2006). Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links. International Journal of Human-Computer Studies, 64(8), 727-743.
[25]         Kosko, B. (1986). Fuzzy cognitive maps. International Journal of man-machine studies, 24(1), 65-75.
[26]         Papageorgiou, E. I., & Kannappan, A. (2012). Fuzzy cognitive map ensemble learning paradigm to solve classification problems: Application to autism identification. Applied Soft Computing, 12(12), 3798-3809.
[27]         Rezaee, M. J., Yousefi, S., & Babaei, M. (2017). Multi-stage cognitive map for failures assessment of production processes: An extension in structure and algorithm. Neurocomputing, 232, 69-82.
[28]         Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359.
[29]         Ashraf, M., & Aksoy, A. (2015). Phytoremediation for Green Energy. M. Öztürk, & M. S. A. Ahmad (Eds.). Springer.
[30]         Mensah, A. K. (2015). Role of revegetation in restoring fertility of degraded mined soils in Ghana: A review. International Journal of Biodiversity and Conservation, 7(2), 57-80.
[31]         Ashraf, M., Q̈ztürk, M. A., & Ahmad, M. S. A. (2010). Plant adaptation and phytoremediation. New York: Springer.
[32]         Andersen, R. G. (2006). In situ characterization and quantification of phytoremediation removal mechanisms for naphthalene at a creosote-contaminated site (Doctoral dissertation, Virginia Polytechnic Institute and State University).
[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]         Moosazadeh, A. (2011). Familiarity with important issues in the design and implementation of the water and effluent disposal system of copper mines. Technical Report. The effluent disposal and tailings dam units of Sungun Copper Mine. Report of National Iranian Copper Industries (In Persian).