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

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.

Modelling of the adsorption of urea herbicides by tropical soils with an Adaptive‐Neural‐based Fuzzy Inference System

“…Tanhaei et al 28 reported a neuro‐fuzzy modelling of methyl orange removal by magnetic chitosan nanocomposite. Baziar et al 29 applied ANFIS in simulating the sorption of 4‐chlorophenol from aqueous solutions by persulphate/nano zero valent iron process. Ghaedi and Vafaei 30 reviewed the applications of ANN for adsorption of dyes from aqueous solutions.…”

Reusable Ag@TiO2-Based Photocatalytic Nanocomposite Membranes for Solar Degradation of Contaminants of Emerging Concern

“…In this context, an adaptive neuro-fuzzy inference system (ANFIS) allows for the prediction of water quality parameters, as has been previously demonstrated [37,38]. Additionally, the application of an ANFIS model has been reported to evaluate wastewater treatment processes, namely in the elimination of organic dyes [39,40], oily wastewater [41], chemical additives [36], antibiotics [42] and heavy metals [43,44].…”

“…As the photocatalytic process is somewhat affected by different parameters, simulating and modelling based on conventional mathematical approaches are quite complicated [36]. In this context, an adaptive neuro-fuzzy inference system (ANFIS) allows for the prediction of water quality parameters, as has been previously demonstrated [37,38].…”

Degradation of phenol using US/Periodate/nZVI system from aqueous solutions

“…One of the most well-known methods for determination of persistent organic matter degradation in wastewater treatment is chemical oxygen demand (COD) (Baziar et al, 2018c). Determination of COD in our work is highly important, because of its potential to oxidize the organic pollutants like phenol using dichromate.…”