0.60
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0.186
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0.447
SNIP
Research paper
 
CC-BY 4.0
 
 

Application of Adaptive Neural Fuzzy Inference System and Fuzzy C- Means Algorithm in Simulating the 4-Chlorophenol Elimination from Aqueous Solutions by Persulfate/Nano Zero Valent Iron Process

Ramin Nabizadeh 1, 2,  
Kazem Naddafi 1,  
 
1
Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, IRAN
2
Center for Air Pollution Research, Institute for Environmental Research, Tehran University of Medical Sciences, Tehran, IRAN
Eurasian J Anal Chem 2018;13(1):em03
Online publish date: 2017-11-18
Publish date: 2017-11-18
KEYWORDS:
ABSTRACT:
This study investigated the application of adaptive neural fuzzy inference system (ANFIS) and Fuzzy c- means (FCM) algorithm for the simulation and prediction of 4-chlorophenol elimination in aqueous media by the persulfate/Nano zero valent iron process. The structure of developed model which resulted to the minimum value of mean square error was a Gaussian membership function with a total number 10 at input layer, a linear membership function at output layer and a hybrid optimum method, which is a combination of backpropagation algorithm and least squares estimation, for optimization of Gaussian membership function parameters. The prediction of developed model in elimination 4-chlorophenol was significantly close to the observed experimental results with R2 value of 0.9942. The results of sensitivity analysis indicated that all operating variables had a strong effect on the output of model (4-CP elimination). However, the most effective variable was pH followed by persulfate, NZVI dosage, reaction time and 4-CP concentration. The performance of developed model was also compared with a quadratic model generated in a study by Response Surface Methodology (RSM). The results indicated that the ANFIS-FCM model was superior to the quadratic model in terms of prediction accuracy and capturing the behavior of the process.
 
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