Modeling and optimization of photocatalytic/photoassisted-electro-Fenton like degradation of phenol using a neural network coupled with genetic algorithm

被引：47

作者：

Khataee, A. R. ^{[1
]}

Fathinia, M. ^{[1
]}

Zarei, M. ^{[2
]}

Izadkhah, B. ^{[2
]}

Joo, S. W. ^{[3
]}

机构：

[1] Univ Tabriz, Fac Chem, Dept Appl Chem, Res Lab Adv Water & Wastewater Treatment Proc, Tabriz, Iran

[2] Univ Tabriz, Fac Chem, Dept Appl Chem, Tabriz, Iran

[3] Yeungnam Univ, Sch Mech Engn, Gyongsan 712749, South Korea

来源：

JOURNAL OF INDUSTRIAL AND ENGINEERING CHEMISTRY | 2014年 / 20卷 / 04期

基金：

新加坡国家研究基金会;

关键词：

Carbon nanotubes; Electro-Fenton; Mn2+-catalyzed reaction; Immobilized TiO2 nanoparticles; Artificial neural network; ELECTROCHEMICAL OXIDATION; AQUEOUS-SOLUTION; DECONTAMINATION; MINERALIZATION; CHROMATOGRAPHY; CHLOROPHENOLS; KINETICS; REMOVAL; WATER; ACID;

D O I：

10.1016/j.jiec.2013.08.042

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

Oxidation of phenol in aqueous media using supported TiO2 nanoparticles coupled with photoelectro-Fenton-like process using Mn2+ cations as catalyst of electro-Fenton reaction was studied. The influence of the basic operational parameters such as initial pH of the solution, applied current, initial Mn2+ concentration, initial phenol concentration and kind of ultraviolet (UV) light on the oxidizing efficiency of phenol was studied. An artificial neural network (ANN) model was coupled with genetic algorithm to predict phenol oxidizing efficiency and to find an optimal condition for maximum phenol removal. The findings indicated that ANN provided reasonable predictive performance (R-2 = 0.949). (C) 2013 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved.

引用

页码：1852 / 1860

页数：9

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