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

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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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