Abstract




 
   

IJE TRANSACTIONS B: Applications Vol. 31, No. 8 (August 2018) 1196-1204    Article in Press

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  FUZZY WASTEWATER QUALITY INDEX DETERMINATION FOR ENVIRONMENTAL QUALITY ASSESSMENT UNDER UNCERTAIN AND VAGUENESS CONDITIONS
 
H. Sarkheil, Y. Azimi and S. Rahbari
 
( Received: January 14, 2018 – Accepted in Revised Form: March 09, 2018 )
 
 

Abstract    Utilization of water in different parts of industrial life cycles brings a huge concern on environmental water and wastewater pollutions. In this research, environmental quality assessment of wastewater is studied using fuzzy logic. Fuzzy appliance is due to existance of statistical considerations (including standard deviations), various uncertainties, non-linearity and complexity of functions. A Mamdani fuzzy inference system (FIS) is developed for prediction of a fuzzy wastewater quality index (FWWQI) where four variables of Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Suspended Solids (TSS) and pH are considered. To assess the performance of the proposed index under actual conditions, water quality data of refineries at South Pars Special Economic and Energy Zone, Iran, are employed in the time interval from 2011 to 2014. Findings of this research indicated that only BOD and COD were the dominant pollutants for about 66% and 34% of analyzed time, respectively, which exceeds the standards. Moreover, the time pattern for the output indices represents that FWWQI varied from "Moderate" in 2011 to "Good" in 2014. In addition, comparison of the FWWQI results with two conventional classic methodologies indicated that the proposed fuzzy method well covers the two classic methodologies. Finally, it is noticed that all three proposed WQIs exhibit correspondingly "Good" level in the year 2014. Thus, the time pattern for the parameters and indices express continual improvement as outcome of ISO 14001 and HSE-MS.

 

Keywords    Water Quality Index WQI, Wastewater, Fuzzy inference, Water Pollutants

 

چکیده    استفاده از آب در صنایعمختلف امری اجتناب ناپذیر بوده و می­تواند موجب آلودگی­های زیست محیطی شود. آلودگی پساب­های صنعتی تاثیرات نامطلوبی بر بهداشت عمومی، سامانه­های اکولوژیکی و منابع آب­های سطحی و زیرسطحی دارد. بدین منظور، نظام­های مدیریت زیست محیطیEMSs و نظام مدیریتیکپارچهHSE-MS از روش­های کارآمد ارزیابی­های زیست محیطی و ارزیابی ریسک زیست محیطی بهره می­جویند. در این پژوهش به مطالعه ارزیابی کیفیت زیست محیطی پساب با استفاده از سامانه استنتاج فازی ممدانی پرداخته شده است. متغیرهای ورودی شامل: BOD، COD، TSS و pH بوده و متغیر خروجی شاخص فازی کیفیت پساب FWWQI تعیریف شده است. متغیرها در بازه [0, 100] در پنج دسته تابع عضویت ذوزنقه­ای با عناوین: 1-کیفیت بسیار خوب، 2-کیفیت خوب، 3-کیفیت متوسط، 4-کیفیت بد و 5-کیفیت خطرناک طبقه بندی شده­اند. تعداد قوانین فازی 24 مورد تعیین شده­اند. روش­های کلاسیک:1- شاخص کیفیت آب جهانیGWQI و 2-شاخص وزنی تجمعی کیفیت آب AWWQI جهت مقایسه کارآمدی روش فازی پیشنهادی، مطالعه گردیده­اند. مطالعه موردی مربوط به منطقه ویژه اقتصادی انرژی پارس در بازه زمانی سال میلادی 2011 الی 2014 می­باشد. مقایسه روش شناسی فازی پیشنهادی و روش­های کلاسیک گویای این مطلب است که مقادیر سالیانهFWWQI در مقایسه با GWQI دارای خطای نسبی +13.05% بوده در حالی که مقادیر سالیانهFWWQI در مقایسه با AWWQI دارای خطای نسبی -3.33% برآوردشده­اند. بر اساس روش شناسی­ها، روش فازی به روش وزنی تجمعی نزدیکی بیشتری داشته است. در مطالعه موردی، تنها BOD و COD از محدوده استاندارد خارج گردیده­اند به طوری که BOD با 66% و COD با 34% انحراف از حالت استاندارد به عنوان آلاینده­های محدود کننده تعیین گردیده­اند. بر اساس مطالعه الگوی زمانی شاخص­های خروجی؛ شاخص فازی از سطح کیفیت متوسط در سال 2011 به سطح خوب در سال 2014، شاخص جهانی از سطح مرزیmarginal در 2011 به سطح خوب در 2014 و شاخص وزنی تجمعی از سطح خوب در 2011 به سطح خوب در 2014 بهبود یافته­اند. قابل توضیح است که هر سه مورد شاخص در سال 2014 نمودار سطح کیفیت خوب می­باشند که از جمله مهم ترین دستاوردهای استقرار نظام های مدیریتیISO 14001 و HSE-MS می­باشد.

References   

1.     Sarkheil, H. and Rahbari, S., "Development of case historical logical air quality indices via fuzzy mathematics (mamdani and takagi–sugeno systems), a case study for shahre rey town", Environmental Earth Sciences,  Vol. 75, No. 19, (2016), 1319. doi.org/10.1007/s12665-016-6131-2

2.     Rickwood, C. and Carr, G., "Global drinking water quality index development and sensitivity analysis report", United Nations Environment Programme (UNEP) & Global Environment Monitoring System (GEMS)/Water Programme,  (2007).

3.     Pesce, S.F. and Wunderlin, D.A., "Use of water quality indices to verify the impact of córdoba city (argentina) on suquı́a river", Water Research,  Vol. 34, No. 11, (2000), 2915-2926.

4.     Ŝtambuk-Giljanović, N., "Comparison of dalmatian water evaluation indices", Water Environment Research,  Vol. 75, No. 5, (2003), 388-405.

5.     Sargaonkar, A. and Deshpande, V., "Development of an overall index of pollution for surface water based on a general classification scheme in indian context", Environmental Monitoring and Assessment,  Vol. 89, No. 1, (2003), 43-67.

6.     Liou, S.-M., Lo, S.-L. and Wang, S.-H., "A generalized water quality index for taiwan", Environmental Monitoring and Assessment,  Vol. 96, No. 1, (2004), 35-52.

7.     Liou, S.-M., Lo, S.-L. and Hu, C.-Y., "Application of two-stage fuzzy set theory to river quality evaluation in taiwan", Water Research,  Vol. 37, No. 6, (2003), 1406-1416.

8.     Said, A., Stevens, D.K. and Sehlke, G., "An innovative index for evaluating water quality in streams", Environmental Management,  Vol. 34, No. 3, (2004), 406-414.

9.     Cabanillas, J., Ginebreda, A., Guillén, D., Martínez, E., Barceló, D., Moragas, L., Robusté, J. and Darbra, R.M., "Fuzzy logic based risk assessment of effluents from waste-water treatment plants", Science of the Total Environment,  Vol. 439, No., (2012), 202-210.

10.   Sarkheil, H. and Rahbari, S., "Hse  key performanceindicators in hse-ms establishment and sustainability:  A  case  of  south  pars  gas  complex, iran", International  Journal  of Occupational Hygiene,  Vol. 8, No. 1, (2016), 45-53.

11.   Shuhaimi-Othman, M., Lim, E.C. and Mushrifah, I., "Water quality changes in chini lake, pahang, west malaysia", Environmental Monitoring and Assessment,  Vol. 131, No. 1-3, (2007), 279-292.

12.   Cieszynska, M., Wesolowski, M., Bartoszewicz, M., Michalska, M. and Nowacki, J., "Application of physicochemical data for water-quality assessment of watercourses in the gdansk municipality (south baltic coast)", Environmental Monitoring and Assessment,  Vol. 184, No. 4, (2012), 2017-2029.

13.   Vieira, J., Fonseca, A., Vilar, V.J., Boaventura, R.A. and Botelho, C.M., "Water quality in lis river, portugal", Environmental Monitoring and Assessment,  Vol. 184, No. 12, (2012), 7125-7140.

14.   Dadi, D., Stellmacher, T., Senbeta, F., Van Passel, S. and Azadi, H., "Environmental and health impacts of effluents from textile industries in ethiopia: The case of gelan and dukem, oromia regional state", Environmental Monitoring and Assessment,  Vol. 189, No. 1, (2017), 11. doi.org/10.1007/s10661-016-5694-4

15.   Zadeh, L., "Information and control", Fuzzy sets,  Vol. 8, No. 3, (1965), 338-353.

16.   McKone, T.E. and Deshpande, A.W., Can fuzzy logic bring complex environmental problems into focus? 2005, ACS Publications.

17.   Peche, R. and Rodríguez, E., "Environmental impact assessment by means of a procedure based on fuzzy logic: A practical application", Environmental Impact Assessment Review,  Vol. 31, No. 2, (2011), 87-96.

18.   Lourenço, R., Silva, D., Martins, A., Sales, J., Roveda, S. and Roveda, J., "Use of fuzzy systems in the elaboration of an anthropic pressure indicator to evaluate the remaining forest fragments", Environmental Earth Sciences,  Vol. 74, No. 3, (2015), 2481-2488.

19.   Kamrani, S., Rezaei, M., Amiri, V. and Saberinasr, A., "Investigating the efficiency of information entropy and fuzzy theories to classification of groundwater samples for drinking purposes: Lenjanat plain, central iran", Environmental Earth Sciences,  Vol. 75, No. 20, (2016), 1370.

20.   Hosseini-Moghari, S.-M., Ebrahimi, K. and Azarnivand, A., "Groundwater quality assessment with respect to fuzzy water quality index (fwqi): An application of expert systems in environmental monitoring", Environmental Earth Sciences,  Vol. 74, No. 10, (2015), 7229-7238.

21.   Esty, D.C., Levy, M.A., Srebotnjak, T., de Sherbinin, A., Kim, C.H. and Anderson, B., "Pilot 2006 environmental performance index", New Haven: Yale Center for Environmental Law & Policy,  Vol., No., (2006).

22.   Gharibi, H., Mahvi, A.H., Nabizadeh, R., Arabalibeik, H., Yunesian, M. and Sowlat, M.H., "A novel approach in water quality assessment based on fuzzy logic", Journal of Environmental Management,  Vol. 112, No., (2012), 87-95.

23.   Verlicchi, P., Masotti, L. and Galletti, A., "Wastewater polishing index: A tool for a rapid quality assessment of reclaimed wastewater", Environmental Monitoring and Assessment,  Vol. 173, No. 1, (2011), 267-277.

24.   Sharifzadeh, M. and HosseinAlizadeh, R., "Artificial neural network approach for modeling of mercury adsorption from aqueous solution by sargassum bevanom algae (research note)", International Journal of Engineering-Transactions B: Applications,  Vol. 28, No. 8, (2015), 1124-1133.

25.   Acosta, H., Wu, D. and Forrest, B.M., "Fuzzy experts on recreational vessels, a risk modelling approach for marine invasions", Ecological Modelling,  Vol. 221, No. 5, (2010), 850-863.

26.   Dahiya, S., Singh, B., Gaur, S., Garg, V. and Kushwaha, H., "Analysis of groundwater quality using fuzzy synthetic evaluation", Journal of Hazardous Materials,  Vol. 147, No. 3, (2007), 938-946.

27.   Ocampo-Duque, W., Ferre-Huguet, N., Domingo, J.L. and Schuhmacher, M., "Assessing water quality in rivers with fuzzy inference systems: A case study", Environment International,  Vol. 32, No. 6, (2006), 733-742.

28.   Saffran, K., Canadian water quality guidelines for the protection of aquatic life, ccme water quality index 1, 0. User’s manual. Excerpt from publication no. 1299. 2001, ISBN 1-896997-34-1.

29.   Organization, W., "Guidelines for drinking-water quality”, vol. 1", World Health Organization,  Vol., No., (2004).

30.   Azimi, Y., Osanloo, M., Aakbarpour-Shirazi, M. and Bazzazi, A.A., "Prediction of the blastability designation of rock masses using fuzzy sets", International Journal of Rock Mechanics and Mining Sciences,  Vol. 47, No. 7, (2010), 1126-1140.

31.   Zadeh, L., Fuzzy sets as a basis for a theory of possibilityfuzzy sets and systems: 1 3. 1978, MathSciNet MATH.

32.   Soler-Ruiz, V., "Lógica difusa aplicada a conjuntos imbalanceados: Aplicación a la detección del síndrome de down tesis (doctoral)", Universidad Autónoma de Barcelona, Escuela Técnica Superior de Ingenierías, Departamento de Microelectrónica y Sistemas Electrónicos, Barcelona, doctoralThesis, Bellaterra : Universitat Autònoma de Barcelona, 2007, (2007), http://www.tdx.cat/TDX-0412107-160104

33.   Aliakbari Nouri, F. and Shafiei Nikabadi, M., "Providing  a  fuzzy  expert  system  to  assess  the  maturity  level  of  companies  in manufacturing excellence in the food industry of iran ", International Journal of Engineering,  Vol. Vol. 30, No. 4, (2017), 532-542

34.   Mirzaeian, B., Moallem, A. and Lucas, C., "A fuzzy expert system for predicting the performance of switched reluctance motor", International Journal of Engineering,  Vol. 14, No. 3, (2001), 229-238.

35.   Khezri, R., Hosseini, R. and Mazinani, M., "A fuzzy rule-based expert system for the prognosis of the risk of development of the breast cancer", International Journal of Engineering Transactions A: Basics,  Vol. 27, No. 10, (2014), 1557-1564.


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