Modeling of Treatment of Dairy Wastewaters by Electrocoagulation Process Using Adaptive Neuro-Fuzzy Inference System

Authors

  • Mohammad Abdollahzadeh1 1- Vice-Chancellery of Food and Drug, sabzevar University of Medical Sciences, Sabzevar, Iran.
  • Roshanak Rafiei Nazari2 2- Dept. of Physics, Islamic Azad University, South Tehran Branch, Tehran, Iran.
  • Negar Abasi Bastami3 3- School of Medicine, Shahroud University of Medical Sciences, Shahroud, Iran.
  • Ebrahim Esmaeili4 4- Vice-Chancellery of Food and Drug, Babol University of Medical Sciences, Babol, Iran.
  • Mojtaba Raeisi5 5- Cereal Health Research Center, Golestan University of Medical Sciences, Gorgan, Iran.
  • Majid Arabameri6 6- Vice-Chancellery of Food and Drug, Shahroud University of Medical Sciences, Shahroud, Iran.

DOI:

https://doi.org/10.22100/jkh.v11i3.1398

Keywords:

Dairy wastewater, Adaptive neuro fuzzy inference system, Electrocoagulation.

Abstract

Introduction: Forecasting of the wastewater quality parameters has great importance in modern wastewater treatment methods. One of the main problems in predicting the efficacy of a wastewater treatment is the complexity of physicochemical properties of raw sewage and data differences for different reasons. Modeling of wastewater treatment using Adaptive Neural Fuzzy Inference System (ANFIS)- can help to improve wastewater quality control process. The aim of the study was to modeling of treatment of dairy wastewaters by electrocoagulation process using ANFIS.

Methods: In this study, ANFIS was used to estimate the chemical coagulation of dairy wastewater treatment. The input parameters to the ANFIS model were time, voltage, total suspended solids and boichemical oxygen demand and the output was chemical oxygen demand removal efficiency. Also the membership functions, the number of membership functions and number of learning cycles (Epochs) were used for optimization of different models by trial and error.

Results: The best model was assessed by bell-shaped membership functions with number of membership functions as 3 3 3 3 3 and 300 epochs of training with lowest mean square error(MES) and the best coefficient of determination (R2). The coefficient of determination and MSE of the best ANFIS model were 0.9912 and 0.012, respectively.

Conclusion: Analysis of the model revealed that the ANFIS is a powerful tool to predict the dairy wastewater treatment using electrical coagulation.

References

Borbón B, Oropeza-Guzman MT, Brillas E, Sirés I. Sequential electrochemical treatment of dairy wastewater using aluminum and DSA-type anodes. Environmental Science and Pollution Research 2014;21:8573-84. doi: 10.1007/s11356-014-2787-x

Bensadok K, Benammar S, Lapicque F, Nezzal G. Electrocoagulation of cutting oil emulsions using aluminium plate electrodes. Journal of Hazardous Materials 2008;152: 423-30. doi: 10.1016/j.jhazmat.2007.06.121

Daneshvar N, Oladegaragoze A, Djafarzadeh N. Decolorization of basic dye solutions by electrocoagulation: An investigation of the effect of operational parameters. Journal of Hazardous Materials 2006;129:116-22. doi: 10.1016/j.jhazmat.2005.08.033

Bazrafshan E, Moein H, Kord Mostafapour F, Nakhaie S. Application of electrocoagulation process for dairy wastewater treatment. Journal of Chemistry 2013;2013:1-8. doi: 10.1155/2013/640139

Bazrafshan E, Mostafapour FK, Farzadkia M, Ownagh KA, Mahvi AH. Slaughterhouse wastewater treatment by combined chemical coagulation and electrocoagulation process. PloS One 2012;7:e40108. doi: 10.1371/journal.pone.0040108

Curteanu S, Piuleac CG, Godini K, Azaryan G. Modeling of electrolysis process in wastewater treatment using different types of neural networks. Chemical Engineering Journal 2011;172: 267-76. doi: 10.1016/j.cej.2011.05.104

Gaya MS, Wahab NA, Sam YM, Samsudin SI. ANFIS modelling of carbon and nitrogen removal in domestic wastewater treatment plant. Jurnal Teknologi 2014;67:29-34. doi: 10.11113/jt.v67.2839

Hernández-Ramírez DA, Herrera-López EJ, editors. Artificial neural network modeling of slaughterhouse wastewater removal of COD and TSS by electrocoagulation. In: Advance Trends in Soft Computing. Springer International pub;2014.p.273-80; doi: 10.1007/978-3-319-03674-8_26

Kundu P, Debsarkar A, Mukherjee S. Artificial neural network modeling for biological removal of organic carbon and nitrogen from slaughterhouse wastewater in a sequencing batch reactor. Advances in Artificial Neural Systems 2013;2013:1-15. doi: 10.1155/2013/268064

Tay JH, Zhang X. A fast predicting neural fuzzy model for high-rate anaerobic wastewater treatment systems. Water Research 2000;34:2849-60. doi: 10.1016/S0043-1354(00)00057-9

Association APH. Standard methods for the examination of water and wastewater. American Public Health Association, Washington, DC 1998, 1268.

Jang JSR, Sun CT, editors. Neuro-fuzzy and soft computing: A Computational approach to learning and machine intelligence. United States: Prentice Hall pub;1997. doi: 10.1109/TAC.1997.633847

Published

2016-06-18

Issue

Section

Original Article(s)

How to Cite

Modeling of Treatment of Dairy Wastewaters by Electrocoagulation Process Using Adaptive Neuro-Fuzzy Inference System. (2016). Knowledge and Health in Basic Medical Sciences, 11(3), Page:32-39. https://doi.org/10.22100/jkh.v11i3.1398

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