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
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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
Online publication date: 2017-11-18
Publication date: 2017-11-18
Eurasian J Anal Chem 2018;13(1):em03
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.
Shinde, S., Bhosale, C., & Rajpure, K. (2011). Photocatalytic oxidation of salicylic acid and 4-chlorophenol in aqueous solutions mediated by modified AlFe 2 O 3 catalyst under sunlight. Journal of Molecular Catalysis A: Chemical, 347(1), 65-72.
Deka, B., & Bhattacharyya, K. (2015). Using coal fly ash as a support for Mn (II), Co (II) and Ni (II) and utilizing the materials as novel oxidation catalysts for 4-chlorophenol mineralization. Journal of environmental management, 150, 479-488.
Ai, Z., Yang, P., & Lu, X. (2005). Degradation of 4-chlorophenol by a microwave assisted photocatalysis method. Journal of hazardous materials, 124(1), 147-152.
Yan, P. (2016). Photoelectrochemical sensing of 4-chlorophenol based on Au/BiOCl nanocomposites. Talanta, 156, 257-264.
Pera-Titus, M., García-Molina, V., Baños, M. A., Giménez, J., & Esplugas, S. (2004). Degradation of chlorophenols by means of advanced oxidation processes: a general review. Applied Catalysis B: Environmental, 47(4), 219-256.
Naghan, D. J. (2015). Parameters effecting on photocatalytic degradation of the phenol from aqueous solutions in the presence of ZnO nanocatalyst under irradiation of UV-C light. Bulgarian Chemical Communications, 47, 14-18.
Sepehri, A. (2016). Modified ozonation process performance with the nanoparticles of copper in disinfection of leachate. Journal of Environmental Science and Technology, 9(1), 157-163.
Rezaii Mofrad, M. R. (2016). Removal of methyl orange from synthetic wastewater using nano-MgO and nano-MgO/UV combination. Desalination and Water Treatment, 57(18), 8330-8335.
Salarian, A.-A. (2016). N-doped TiO 2 nanosheets for photocatalytic degradation and mineralization of diazinon under simulated solar irradiation: Optimization and modeling using a response surface methodology. Journal of Molecular Liquids, 220, 183-191.
Yousefi, N. (2016). Application of nanofilter in removal of phosphate, fluoride and nitrite from groundwater. Desalination and Water Treatment, 57(25), 11782-11788.
Mirzaei, N. (2016). Sorption of acid dye by surfactant modificated natural zeolites. Journal of the Taiwan Institute of Chemical Engineers, 59, 186-194.
Zhao, L. (2016). Simultaneous removal of bisphenol A and phosphate in zero-valent iron activated persulfate oxidation process. Chemical Engineering Journal, 303, 458-466.
Li, X. (2017). Pre-magnetized Fe 0/persulfate for notably enhanced degradation and dechlorination of 2, 4-dichlorophenol. Chemical Engineering Journal, 307, 1092-1104.
Temiz, K., Olmez-Hanci, T., & Arslan-Alaton, I. (2016). Zero-valent iron-activated persulfate oxidation of a commercial alkyl phenol polyethoxylate. Environmental technology, 37(14), 1757-1767.
Karami, M. (2010) Degradation of Reactive Red 198 (RR198) from aqueous solutions by advanced oxidation processes (AOPS): O3, H2O2/O3 and H2O2/ultrasonic. Chemical Engineering, 35(10), 10-23.
Elmolla, E. S., Chaudhuri, M., & Eltoukhy, M. M. (2010). The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process. Journal of hazardous materials, 179(1), 127-134.
Chamarro, E., Marco, A., & Esplugas, S. (2001). Use of Fenton reagent to improve organic chemical biodegradability. Water research, 35(4), 1047-1051.
Maran, J. P. (2013). Artificial neural network and response surface methodology modeling in mass transfer parameters predictions during osmotic dehydration of Carica papaya L. Alexandria Engineering Journal, 52(3), 507-516.
Civelekoglu, G. (2009). Modelling of COD removal in a biological wastewater treatment plant using adaptive neuro-fuzzy inference system and artificial neural network. Water Science and Technology, 60(6), 1475-1487.
Wan, J. (2011). Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system. Applied Soft Computing, 11(3), 3238-3246.
Salehi, F., & Razavi, S. M. (2016). Modeling of waste brine nanofiltration process using artificial neural network and adaptive neuro-fuzzy inference system. Desalination and Water Treatment, 57(31), 14369-14378.
Baziar, M. (2017). MWCNT-Fe 3 O 4 as a superior adsorbent for microcystins LR removal: Investigation on the magnetic adsorption separation, artificial neural network modeling, and genetic algorithm optimization. Journal of Molecular Liquids. In Press.
Abdulshahed, A. M. (2015). Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera. Applied Mathematical Modelling, 39(7), 1837-1852.
Jang, J.-S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
Hung, M.-C., & Yang, D.-L. (2001). An efficient fuzzy c-means clustering algorithm. In Data Mining, ICDM 2001, Proceedings IEEE International Conference on. IEEE.
Shams, M. (2016). Adsorption of phosphorus from aqueous solution by cubic zeolitic imidazolate framework-8: Modeling, mechanical agitation versus sonication. Journal of Molecular Liquids, 224, 151-157.
Deng, J. (2014). Zero-valent iron/persulfate (Fe0/PS) oxidation acetaminophen in water. International Journal of Environmental Science and Technology, 11(4), 881-890.
Hussain, I., Zhang, Y., & Huang, S. (2014). Degradation of aniline with zero-valent iron as an activator of persulfate in aqueous solution. Rsc Advances, 4(7), 3502-3511.
Zhao, J. (2010). Enhanced oxidation of 4-chlorophenol using sulfate radicals generated from zero-valent iron and peroxydisulfate at ambient temperature. Separation and Purification Technology, 71(3), 302-307.
Najah, A. (2014). Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring. Environmental Science and Pollution Research, 21(3), 1658-1670.