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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,  
Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, IRAN
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
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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