Introduction and Assessment of a New Effluents Usage Method

Document Type : Research Paper

Authors

1 Irrigation and Reclamation Eng. UT

2 University of Tehran

Abstract

The increase in water demand from different aspects of water usage highlights the importance of the treated wastewater as a new water resource. The main aim of the current paper is to present a new method to specify the best applications for effluents involving fuzzy and entropy approaches based on the characteristics of effluents. To achieve this goal, the effluents’ characteristics data of Arak city wastewater plant were utilized from 2013 to 2017. Also, six possible consumption options were determined and considered. Then, the appropriate parameters that were involved with the new derived indices were selected and the best options were chosen. The mentioned indices are named the Fuzzy Effluents Quality Index (FEQI) and the Entropy Effluents Quality Index (EEQI). The results of the research indicate that not only the proposed indices are sensitive to the input parameters, but they also classify the effluents correctly. Moreover, the results illustrated that the quality of Arak effluents in the last 5 years have improved. As in the last year, according to the new indices, the effluents can be used in industrial, environmental, fodder production, cooked vegetables, oil seed production, and artificial groundwater recharge systems.

Keywords

Main Subjects

References

Amiri, V. Rezaei, M. and Sohrabi, N, (2014). Groundwater quality assessment using entropy weighted water quality index (EWQI) in Lenjanat, Iran. Environ Earth Sci. (2014) 72,3479-3490.
Dahiya, S.B. Singh, S. Gaur, V. Garg, K. and Kushwaha, H.S. (2007). Analysis of groundwater quality using fuzzy synthetic evaluation. Journal of Hazardous Material. 147(3), 946-938.
Environmental Criteria of Treated WasteWater and Return Flow Reuse, No. 535, Ministry of Energy
EPA. National Recommended Water Quality Criteria, (2006).
Fang Fang, Li-Li Qiao. Bing-Jie Ni, Jia-Shun Cao and Han-Qing Yu. (2017). Quantitative evaluation on the characteristics of activated sludge granules and flocs using a fuzzy entropy-based approach. Scientific Reports. 7,42910.
FAO/UNESCO. (1973). Irrigation, Drainage, and Salinity. An International Sourcebook.
Gharibi H. Mahvi A.H. Nabizadeh R. Arabalibeik H. Yunesian M. and Sowlat, M.H. (2012). A novel approach in water quality assessment based on fuzzy logic. Journal of environmental management, 112, 87-95.
Guey-Shin, Sh. Bai-You, Ch. Chi-Ting, Ch., Pei-Hsuan, Y, and Tsun-Kuo, Ch, (2011). Applying Factor Analysis Combined with Kriging and Information Entropy Theory for Mapping and Evaluating the Stability of Groundwater Quality Variation in Taiwan Int. J. Environ. Res. Public Health. (8), 1084-1109.
Hosseini-Moghari, S M H. Ebrahimi, K. and Azarnivand A. (2015). Groundwater quality assessment with respect to fuzzy water quality index (FWQI): an application of expert systems in environmental monitoring. Environ Earth Sci.
Icaga Y. (2007). Fuzzy evaluation of water quality classification. Ecological Indicators, 7(3), 710-718.
Institute of Standards and Industrial Research of Iran, Drinking water Physical and chemical specifications, ICS:13.060.20.
Institute of Standards and Industrial Research of Iran, Specifications of industrial effluents, ISIRI NUMBER 2439.
Jian-Hua, W. Pei-Yue, L. and Hui, Q. (2011). Groundwater Quality in Jingyuan County, a Semi-Humid Area in Northwest China. E-Journal of Chemistry.  8(2),787-793.
Kawachi, T. Maruyama, T. and Singh, V.P. (2001). Rainfall entropy for delineation of water resources zones in Japan, J. Hydrol. (246), 36-44.
Machine Studies, 8(6),  669-678.
Mamdani E.H. (1976). Advances in the linguistic synthesis of fuzzy controllers. International Journal of Man.
Mohammadi Ghaleni, M. and Ebrahimi, K. (2015). Effects of human activities and climate variability on water resources in the Saveh plain, Iran. Environ Monit Assess. 187:35, DOI 10.1007/s10661-014-4243-2
Nasseri, M, Tajrishy, M. Nikoo, M R. and Zaherpour, J. (2013). Recognition and Spatial Mapping of Multivariate Groundwater Quality Index using Combined Fuzzy Method, Journal of Water & Wastewater, Issue 85 (in Farsi)
Niko, M. and Karachian, R. (2010). Water quality evaluation of rivers using hybrid fuzzy inference system and bayesian networks- Jajrood River. The 4th Environmental Engineering Expert Conference.
Ozkul, S. Harmancioglu, N.B. and Singh, V.P. (2000). "Entropy-based assessment of water quality monitoring networks". J. Hydrol. Eng. (5), 90-100.
Pei-Yue, L. Hui, Q. and Jian-Hua, W. (2010). Groundwater Quality Assessment Based on Improved Water Quality Index in Pengyang County, Ningxia, Northwest China. E-Journal of Chemistry. 7(S1): S209-S216.
Sadat Noori, S.M., Ebrahimi, K. and Liaghat A.M., (2013). Groundwater quality assessment using the Water Quality Index and GIS in Saveh-Nobaran aquifer, Iran. Environ Earth Sci. DOI 10.1007/s12665-013-2770-8.
Sadat Noori, S.M., Ebrahimi, K., Liaghat A.M. and Hoorfar, A.H., (2012). Comparison of different geostatistical methods to estimate groundwater level at different climatic periods. Water and Environment Journal. DOI:10.1111/j.1747-
Shannon, CE. (1948),”A mathematical theory of 6593.2012.00321.x.  communications, I and II,”Bell Sys. Technol. J. 27, 379-423.
Sugeno M. (1985). Industrial applications of fuzzy control. Elsevier Science Inc.
Verlicchi, P. Masotti , L. and Gallett, A. (2010), Wastewater polishing index: a tool for a rapid quality assessment of reclaimed wastewater, Environ Monit Assess (2011) 173, 267–277.
WHO. (2004). Guidelines for drinking water quality: training pack. WHO, Geneva, Switzerland.
Yager R.R. and Filev D.P. (1994). Essentials of fuzzy modeling and control. John Wiley and Sons, New York.
Zadeh L.A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
Iranian Journal of Soil and Water Research
Volume 48, Issue 5
January 2018
Pages 963-974

Files

Share

How to cite

Statistics