Forecasting Wastewater Treatment Results with an ANFIS Intelligent System


  • M. Mahshidnia Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran
  • A. Jafarian Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran
Volume: 6 | Issue: 5 | Pages: 1175-1181 | October 2016 |


Wastewaters caused by industrial and manufacturing production containing pollutants which beside degradation and depletion of natural resources, impose excessive pressure on the Earth's ecosystems and exacerbate water shortages. One of the pollutants is a toxic substance named Malachite Green (MG). Wastewater treatment means to obtain usable water by separating contaminants of contaminated water. One of its main purposes is the recovery and re-use of wastewater for a variety of uses including agriculture and aquaculture, especially in arid and semi-arid countries, as well as providing environmental protection. The main purpose of the present study was to investigate MG separation efficiency by nano composite materials. Poly-aniline was covered on Wheat Husk Ash in order to prepare this type of nano composite. The material was analyzed by X-ray radiation and scanned by an electron microscope. The level of separation depends on the initial value of wheat husk ash and poly-aniline and the initial concentration of MG and the intensity of ultraviolet radiation and radiation time. The effect of these parameters was investigated and optimum operating conditions were obtained. An adaptive neural fuzzy intelligent system was used to forecast the results of the MG separation process. The comparison between the results forecasted by the designed model and experimental data strengthens the validity of the process.


Malachite Green (MG), industrial wastewater treatment, adaptive neural fuzzy intelligent system, ANFIS


Download data is not yet available.


J. Wan, M. Huang, Y. Ma, W. Guo, Y. Wang, H. Zang, W. Li, X. Sun, “Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system”, Appl. Soft Computing., Vol. 11, No.3, pp. 3238-3246, 2011 DOI:

H. Honggui, L. Ying, Q. Junfei, “A fuzzy neural network approach for online fault detection in waste water treatment process”, Computers. Elec. Eng., Vol. 40, No.7, pp. 2216-2226, 2014 DOI:

S. Pommier, D. Chenu, M. Quintard, X. Lefebvre, “A logistic model for the prediction of the influence of water on the solid waste methanization in landfills”, Biotechnology and Bioengineering., Vol. 97, No.3, pp. 473-482, 2007 DOI:

S. Pavlidou, C. Papaspyrides, “A review on polymer-layered silicate nanocomposites”, Prog. In. Pol. Sci., Vol. 33, No.12, pp. 1119-1198, 2008 DOI:

I. Michael, A. Panagi, L. A. Ioannou, Z. Frontistis, D. Fatta-Kassinos, “Utilizing solar energy for the purification of olive mill wastewater using a pilot-scale photocatalytic reactor after coagulation-flocculation”, Wat. Res., Vol. 60, pp. 28-40, 2014 DOI:

A. Al-Kdasi, A. Idris, K. Saed, C. Guan, “Treatment of textile wastewater by advanced oxidation processes—a review”, Global Nest: the Int. J., Vol. 6, No. 3, pp. 222-230, 2004 DOI:

M. Henze, P. Harremoes, J. La Cour Jansen, E. Arvin, Wastewater treatment: biological and chemical processes, Springer Science & Business Media, 2001 DOI:

B. Guterstam, “Demonstrating ecological engineering for wastewater treatment in a Nordic climate using aquaculture principles in a greenhouse mesocosm”, Eco. Eng., Vol. 6, No. 1, pp. 73-97, 1996 DOI:

J. Hinge, H. Stewart, C. Etnier, B. Guterstam, “Solar wastewater treatment in Denmark: demonstration project at Danish Folkecenter for Renewable Energy”, Eco. Eng. Waste Water., pp. 169-171, 1991

S. Peterson, J. Teal, “The role of plants in ecologically engineered wastewater treatment systems”, Eco. Eng., Vol. 6, No. 1, pp. 137-148, 1996 DOI:

APHA, AWWA, WEF (1995) Standard methods for the examination of water and wastewater 19th ed, Wash. DC.

K. Larsdotter, G. Dalhammer, “Phosphorus removal from wastewater by microalgae in a greenhouse in Sweden”, WEMS. Env. Bio., pp. 183-188, 2004

M. Ghorbani, H. Eisazadeh, “Removal of COD, color, anions and heavy metals from cotton textile wastewater by using polyaniline and polypyrrole nanocomposites coated on rice husk ash”, Composites Part B. Eng., Vol. 45, No. 1, pp. 1-7, 2013 DOI:

A. Buasri, N. Chaiyut, K. Phattarasirichot, P. Yongbut, L. Nammueng, “Use of natural clinoptilolite for the removal of lead (II) from wastewater in batch experiment”, Chiang. Mai. Sci., Vol. 35, No. 3, pp. 447-456, 2008

R. Noori, A. Faokhnia, S. Morid, H. Riahi Madvar, “Effect of input variables preprocessing in artificial neural network on monthly flow prediction by PCA and wavelet transformation”, J. Water and Wastewater., Vol. 1, pp. 13-22, 2009

M. Bahram, R. Talebi, A. Naseri, S. Nouri, “Modeling and Optimization of Removal of Rhodamine-B from Wastewaters by Adsorption Modified Clay”, Chiang. Mai. Sci., Vol. 41, No. 5.2, pp. 1230-1240, 2014

L. Xiong, A. Shamseldin, “A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi-Sugeno fuzzy system”, Hyd., Vol. 245, No. 1, pp. 196-217, 2001 DOI:

R. Noori, R. Kerachian, A. Khodadadi, A. Shakibinia, “Assessment of importance of water quality monitoring stations using principal component and factor analysis: A case study of the karoon river.” J. of Water and Wastewater, 63, 60-69, 2007

Y. Ouyang, “Evaluation of river water quality monitoring stations by principal component analysis”, Wat. Res., Vol. 39, No. 12, pp. 2621-2635, 2005 DOI:

R. Noori, G. Hoshyaripour, K. Ashrafi, B. Araabi, “Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration”, Atm. Env., Vol. 44, No. 4, pp. 476-482, 2010 DOI:

F. Ghanbary, A. Jafarian, “Polyaniline/Wheat Husk Ash Nanocomposite Preparation and Modeling Its Removal Activity With an Artificial Neural Network”, Vol. 42, No. 3, pp. 1-12, 2015

M. Enhessari, A. Parvizi, K. Ozaee, E. Karamali, “Magnetic properties and heat capacity of CoTio3 nanopowders prepared by stearic acid gel method”, Journal of Experimental Nanoscience, Vol. 5, pp. 61-68, 2010 DOI:

K. Gupta, P. C. Janab, A. K. Meikapa, “Optical and electrical transport properties of polyaniline–silver nanocomposite”, Journal of Synthetic Metals, Vol. 160 ,pp. 1566–1573, 2010 DOI:

I. Bekri, E. Srasra, “Solid-state synthesis and electrical properties of polyaniline/Cu-montmorillonite nanocomposite”, Materials Research Bulletin 45, pp. 1941-1947, 2010 DOI:

.MGhorbani, M. Soleimani Lashkenari, H. Eisazadeh, “Application of polyaniline nanocomposite coated on rice husk ash for removal of Hg(II) from aqueous media”, Synthetic Metals., Vol. 161, pp. 1430– 1433, 2011 DOI:

M. Omraei, H. Esfandian, R. Katal, M. Ghorbani, “Study of the removal of Zn(II) from aqueous solution using polypyrrole nanocomposite”, Desalination, Vol. 271, pp. 248–256, 2011 DOI:

M. Ghorbani, H. Eisazadeh, “Synthesis and characterization of chemical structure and thermal stability of nanometer size polyaniline and polypyrrole coated on rice husk”, Synthetic Metals, Vol. 162, pp. 527– 530, 2012 DOI:

E. N. Konyushenko, M. Omastova, Z. Spitalsky, M. Micusik, L. Krupa, “Thin21- polyaniline and polyaniline/carbon nanocomposite films for gas sensing”, Journal of Thin Solid Films,Vol. 519, pp. 4123–4127, 2011 DOI:

W. Wang, A. Wang, “Nanocomposite of carboxymethyl cellulose and attapulgite as a novelpH-sensitive superabsorbent: Synthesis, characterization and properties”, Carbohydrate Polymers, Vol. 82, pp. 83–91, 2010 DOI:

J. Jang, “ANFIS: adaptive-network-based fuzzy inference system”, Sys. Man, Cyb., Vol. 23, No. 3, pp. 665-685, 1993 DOI:

J. Liang, L. Bai, Ch. Dang, F. Sao, “The-Means-Type Algorithms Versus Imbalanced Data Distributions”, Fuzzy Systems, Vol. 20, No. 4, pp. 728-745, 2012 DOI:

H. Izakian, A. Abraham, “Fuzzy C-means and fuzzy swarm for fuzzy clustering problem”, Exp. Sys. App., Vol. 38, No. 3, pp. 1835-1838, 2011 DOI:

D. Kim, K. Lee, D. Lee, K. Lee, “A kernel-based subtractive clustering method”, Pattern. Rec. Let., Vol. 26, No. 7, pp. 879-891, 2005 DOI:

N. Chen, “A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering”, Inf. Sci., Vol. 220, pp. 180-195, 2013 DOI:


How to Cite

M. Mahshidnia and A. Jafarian, “Forecasting Wastewater Treatment Results with an ANFIS Intelligent System”, Eng. Technol. Appl. Sci. Res., vol. 6, no. 5, pp. 1175–1181, Oct. 2016.


Abstract Views: 481
PDF Downloads: 165

Metrics Information
Bookmark and Share